Systemic lupus erythematosus

ABSTRACT

This document relates to methods and materials involved in diagnosing SLE. For example, this document relates to methods and materials involved in diagnosing SLE, diagnosing severe SLE, and assessing a mammal&#39;s susceptibility to develop severe SLE. For example, this document provides nucleic acid arrays that can be used to diagnose SLE in a mammal. Such arrays can allow clinicians to diagnose SLE based on a simultaneous determination of the expression levels of many genes that are differentially expressed in SLE patients as compared to healthy controls. In addition, methods and materials for assessing SLE activity, determining the likelihood of experiencing active SLE, and detecting SLE treatment effectiveness are provided herein.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional of application Ser. No. 11/251,589,filed Oct. 13, 2005, which claims the benefit of U.S. ProvisionalApplication Ser. No. 60/618,442, filed Oct. 13, 2004, both of which areincorporated herein by reference in their entirety.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

This invention was made with government support under grant no.N01-AR12256, awarded by the National Institutes of Health. Thegovernment has certain rights in the invention.

BACKGROUND

1. Technical Field

This document relates to methods and materials involved in diagnosingsystemic lupus erythematosus (SLE). For example, this document relatesto methods and materials involved in diagnosing SLE, diagnosing severeSLE, assessing a mammal's susceptibility to develop severe SLE, andassessing SLE activity.

2. Background Information

SLE is a chronic, inflammatory autoimmune disease characterized by theproduction of autoantibodies having specificity for a wide range ofself-antigens. SLE autoantibodies mediate organ damage by directlybinding to host tissues and by forming immune complexes that deposit invascular tissues and activate immune cells. Organs targeted in SLEinclude the skin, kidneys, vasculature, joints, various blood elements,and the central nervous system (CNS). The severity of disease, thespectrum of clinical involvement, and the response to therapy varywidely among patients. This clinical heterogeneity makes it challengingto diagnose and manage lupus.

SUMMARY

This document relates to methods and materials involved in diagnosingSLE. For example, this document relates to methods and materialsinvolved in diagnosing SLE, diagnosing severe SLE, assessing a mammal'ssusceptibility to develop severe SLE, and assessing SLE activity. Forexample, this document provides nucleic acid arrays that can be used todiagnose SLE in a mammal. Such arrays can allow clinicians to diagnoseSLE based on a determination of the expression levels of many genes thatare differentially expressed in SLE patients as compared to healthycontrols. This document also provides methods and materials that can beused to assess SLE activity. Assessing SLE activity can allow cliniciansto identify patients with active SLE. In addition, this documentprovides methods and materials that can be used to assess the likelihoodthat a patient will experience active SLE. For example, a patient foundto have cells expressing one or more genes listed in Table 19 at a levelthat is greater than or less than the average level observed in controlcells can be classified as being likely to experience active SLE. Thisdocument also provides methods and materials that can be used todetermine whether or not a mammal responds to an SLE treatment. Forexample, patients receiving an SLE treatment (e.g., an anti-IFNtreatment) who are found to no longer express one or more genes withinan IFN signature at a level greater than or less than the average levelobserved in control cells can be classified as responding to that SLEtreatment.

In addition, this document provides methods and materials involved indiagnosing SLE conditions that are accompanied by activation of aninterferon pathway. For the purpose of this document, the term “SLEaccompanied by activation of an interferon pathway” (abbreviated“SLE-AIP”) refers to any SLE condition that coexists with or is causedby activation of an interferon pathway. Activation of an interferonpathway refers to a state where interferon-regulated genes that areup-regulated in response to interferon are up-regulated, and whereinterferon-regulated genes that are down-regulated in response tointerferon are down-regulated. Typically, activation of an interferonpathway results in the presence of a gene expression profile that issimilar to the gene expression profile observed in cells that weretreated with interferon. An interferon pathway can be activatedregardless of the presence or absence of detectable levels ofinterferon. For example, an SLE patient can have low levels ofdetectable interferon while exhibiting a gene expression profilecharacteristic of an activated interferon pathway. Such an SLE patientcan be diagnosed as having SLE-AIP.

Diagnosing patients as having SLE-AIP can help clinicians determineappropriate treatments for those patients. For example, a clinician whodiagnoses a patient as having SLE-AIP can treat that patient withmedication that improves both the patient's SLE symptoms and aberrantactivation of an interferon pathway. In some cases, a single medicationcan be used to reverse a patient's activation of an interferon pathwaysuch that the patient's SLE symptoms are reduced or relieved. Thus,treating a patient having SLE-AIP by modulating the level of interferonpathway activation can improve that patient's health and quality of lifeby, for example, reducing the symptoms associated with SLE.

Typically, a diagnosis of SLE can be made on the basis of 11 criteriadefined by the American College of Rheumatology (ACR). These criteriainclude malar rash, discoid rash, photosensitivity, oral ulcers,arthritis, serositis, renal disorder, neurologic disorder, hematologicdisorder, immunologic disorder, and antinuclear antibody (Tan et al.(1982) Arthritis Rheum. 25:1271-1277). A mammal (e.g., a human) can beclinically diagnosed with SLE if he or she meets at least four of theeleven criteria. The term “severe SLE” as used herein refers to an SLEcondition where the patient has one or more of the following: renal,central nervous system, or hematologic involvement.

This document is based, in part, on the discovery of genes that aredifferentially expressed between SLE patients and healthy controls. Thisdocument also is based, in part, on the discovery that the expressionlevels of these genes can be used to distinguish mammals with SLE fromhealthy mammals. For example, the expression levels for the genes listedin Table 1 can be assessed to diagnose SLE. In addition, this documentis based, in part, on the discovery that a portion of SLE patients canhave SLE associated with or caused by activation of an interferonpathway. For example, SLE patients having severe SLE can be, at leastpartially, dependent upon the presence of an activated interferonpathway. Further, this document is based, in part, on the discovery ofgenes that are differentially expressed between SLE-AIP patients and SLEpatients not associated with an activated interferon pathway. Forexample, the expression levels for the genes listed in Table 4 can beassessed to diagnose SLE-AIP.

For the purpose of this document, the term “IFN signature 1” as usedherein refers to an expression profile where one or more (e.g., two,three, four, five, six, seven, eight nine, ten, 15, 20, 25, 30, 35, 40,45, 50, 55, 60, 65, or more) of the genes listed in Table 5 areoverexpressed as compared to control cells from a control mammal (e.g.,PBMCs from a healthy human). In some cases, the IFN signature 1 can bean expression profile where 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100percent of the genes listed in Table 5 are overexpressed as compared tocontrol cells from a control mammal. The term “activity signature 1” asused herein refers to an expression profile where one or more (e.g.,two, three, four, five, six, seven, eight nine, ten, 15, 20, 25, 30, 35,40, 45, 50, 55, 60, 65, or more) of the genes listed in Table 16 aredifferentially expressed as compared to control cells from a controlmammal (e.g., PBMCs from a healthy human). In some cases, the activitysignature 1 can be an expression profile where 10, 20, 30, 40, 50, 60,70, 80, 90, or 100 percent of the genes listed in Table 16 aredifferentially expressed as compared to control cells from a controlmammal. The term “activity signature 2” as used herein refers to anexpression profile where one or more (e.g., two, three, four, five, six,seven, eight nine, ten, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, ormore) of the genes listed in Table 17 are differentially expressed ascompared to control cells from a control mammal (e.g., PBMCs from ahealthy human). In some cases, the activity signature 2 can be anexpression profile where 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100percent of the genes listed in Table 17 are differentially expressed ascompared to control cells from a control mammal. The term “activitysignature 3” as used herein refers to an expression profile where one ormore (e.g., two, three, four, five, six, seven, eight nine, ten, 15, 20,25, 30, 35, 40, 45, 50, 55, 60, 65, or more) of the genes listed inTable 19 are differentially expressed as compared to control cells froma control mammal (e.g., PBMCs from a healthy human). In some cases, theactivity signature 3 can be an expression profile where 10, 20, 30, 40,50, 60, 70, 80, 90, or 100 percent of the genes listed in Table 19 aredifferentially expressed as compared to control cells from a controlmammal.

In one aspect, this document features a method for diagnosing severesystemic lupus erythematosus. The method can include (a) determiningwhether or not a mammal contains cells that express at least 2 of thegenes listed in Table 5 to an extent greater than or less than theaverage level of expression exhibited in control cells from one or morecontrol mammals, wherein the mammal and the one or more control mammalsare from the same species; and (b) diagnosing the mammal as havingsevere systemic lupus erythematosus if the mammal contains the cells anddiagnosing the mammal as not having severe systemic lupus erythematosusif the mammal does not contain the cells. The mammal can be a human. Theone or more control mammals can be healthy humans. The one or morecontrol mammals can be humans with mild systemic lupus erythematosus.The cells and the control cells can be peripheral blood mononuclearcells. The method can include determining whether or not the mammalcontains cells that express at least 5 of the genes or at least 10 ofthe genes to an extent greater than or less than the level of expressionexhibited in the control cells. The extent can be greater than or lessthan the average level of expression exhibited in control cells from atleast 10 control mammals or from at least 20 control mammals. Thedetermining step can include measuring the level of mRNA expressed fromat least 2 of the genes or from at least 5 of the genes.

In another aspect, this document features a method for assessing thepredisposition of a mammal to develop severe systemic lupuserythematosus. The method can include (a) determining whether or not themammal contains cells that express at least 2 of the genes listed inTable 5 to an extent greater than or less than the average level ofexpression exhibited in control cells from one or more control mammals,wherein the mammal and the one or more control mammals are from the samespecies, and (b) classifying the mammal as being susceptible to developsevere systemic lupus erythematosus if the mammal contains the cells andclassifying the mammal as not being susceptible to develop severesystemic lupus erythematosus if the mammal does not contain the cells.The mammal can be a human. The one or more control mammals can behealthy humans. The one or more control mammals can be humans with mildsystemic lupus erythematosus. The cells and the control cells can beperipheral blood mononuclear cells. The method can include determiningwhether or not the mammal contains cells that express at least 5 of thegenes or at least 10 of the genes to an extent greater than or less thanthe level of expression exhibited in the control cells. The extent canbe greater than or less than the average level of expression exhibitedin control cells from at least 10 control mammals or from at least 20control mammals. The determining step can include measuring the level ofmRNA expressed from at least 2 of the genes or from at least 5 of thegenes.

In another aspect, this document features a method for diagnosingsystemic lupus erythematosus in a mammal. The method can include (a)determining whether or not the mammal contains cells that express atleast 10 of the genes listed in Tables 5, 7, 8, 9, 16, 17, and 19 to anextent greater than or less than the average level of expressionexhibited in control cells from one or more control mammals, wherein themammal and the one or more control mammals are from the same species,and (b) diagnosing the mammal as having systemic lupus erythematosus ifthe mammal contains the cells and diagnosing the mammal as not havingsystemic lupus erythematosus if the mammal does not contain the cells.

In another aspect, the method for diagnosing systemic lupuserythematosus in a mammal can include (a) determining whether or not themammal contains cells that express at least 5 of the genes listed inTable 7 to an extent greater than the average level of expressionexhibited in control cells from one or more control mammals, wherein themammal and the one or more control mammals are from the same species,and (b) diagnosing the mammal as having systemic lupus erythematosus ifthe mammal contains the cells and diagnosing the mammal as not havingsystemic lupus erythematosus if the mammal does not contain the cells.

In still another aspect, the method for diagnosing systemic lupuserythematosus in a mammal can include (a) determining whether or not themammal contains cells that express at least 5 of the genes listed inTable 8 to an extent less than the average level of expression exhibitedin control cells from one or more control mammals, wherein the mammaland the one or more control mammals are from the same species, and (b)diagnosing the mammal as having systemic lupus erythematosus if themammal contains the cells and diagnosing the mammal as not havingsystemic lupus erythematosus if the mammal does not contain the cells.

In yet another aspect, this document features a nucleic acid arraycontaining at least 5 nucleic acid molecules, wherein each of the atleast 5 nucleic acid molecules has a different nucleic acid sequence,and wherein at least 50 percent of the nucleic acid molecules of thearray include a sequence from a gene selected from the group consistingof the genes listed in Tables 5, 7, 8, 9, 16, 17, and 19. The array cancontain at least 10 nucleic acid molecules, wherein each of the at least10 nucleic acid molecules has a different nucleic acid sequence. Thearray can contain at least 20 nucleic acid molecules, wherein each ofthe at least 20 nucleic acid molecules has a different nucleic acidsequence. The array can contain at least 50 nucleic acid molecules,wherein each of the at least 50 nucleic acid molecules has a differentnucleic acid sequence. Each of the nucleic acid molecules that contain asequence from a gene selected from the group can include no more thanthree mismatches. At least 75 percent (e.g., at least 95 percent) of thenucleic acid molecules of the array can contain a sequence from a geneselected from the group. The array can contain glass.

In yet another aspect, this document features a method for identifying amammal having severe systemic lupus erythematosus. The method comprises,or consist essentially of, (a) determining whether or not a mammalcontains cells having an IFN signature 1, and (b) classifying saidmammal as having severe systemic lupus erythematosus if the mammalcontains the cells and classifying the mammal as not having severesystemic lupus erythematosus if the mammal does not contain the cells.The mammal can be a human. The cells can be peripheral blood mononuclearcells. The IFN signature 1 can comprise, or consist essentially of, atleast 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, or 100 percent of thegenes listed in Table 5.

In yet another aspect, this a method for assessing systemic lupuserythematosus disease activity. The method comprises, or consistessentially of, (a) determining whether or not a mammal contains cellshaving an activity signature 1, an activity signature 2, or an activitysignature 3, and (b) classifying the mammal as having active systemiclupus erythematosus disease if the mammal contains the cells andclassifying the mammal as not having active systemic lupus erythematosusdisease if the mammal does not contain the cells. The mammal can be ahuman. The cells can be peripheral blood mononuclear cells. The methodcan comprise determining whether or not the mammal contains cells havingthe activity signature 1. The method can comprise determining whether ornot the mammal contains cells having the activity signature 2. Themethod can comprise determining whether or not the mammal contains cellshaving the activity signature 3. The activity signature 1 can comprise,or consist essentially of, at least 10, 20, 30, 40, 50, 60, 70, 80, 90,95, or 100 percent of the genes listed in Table 16. The activitysignature 2 can comprise, or consist essentially of, at least 10, 20,30, 40, 50, 60, 70, 80, 90, 95, or 100 percent of the genes listed inTable 17. The activity signature 3 can comprise, or consist essentiallyof, at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, or 100 percent ofthe genes listed in Table 19.

In yet another aspect, this document features a method for assessingsystemic lupus erythematosus disease activity. The method comprises, orconsists essentially of, (a) determining whether or not a mammalcontains cells that express at least 2 of the genes listed in Table 16,17, or 19 to an extent greater than or less than the average level ofexpression exhibited in control cells from one or more control mammals,wherein the mammal and the one or more control mammals are from the samespecies; and (b) classifying the mammal as having active systemic lupuserythematosus disease if the mammal contains the cells and classifyingthe mammal as not having active systemic lupus erythematosus disease ifthe mammal does not contain the cells. The mammal can be a human. Theone or more control mammals can be healthy humans. The one or morecontrol mammals can be humans with inactive systemic lupuserythematosus. The cells and the control cells can be peripheral bloodmononuclear cells. The method can include determining whether or not themammal contains cells that express at least 5 of the genes or at least10 of the genes to an extent greater than or less than the level ofexpression exhibited in the control cells. The extent can be greaterthan or less than the average level of expression exhibited in controlcells from at least 10 control mammals or from at least 20 controlmammals. The determining step can include measuring the level of mRNAexpressed from at least 2 of the genes or from at least 5 of the genes.

In yet another aspect, this document a method for identifying a mammallikely to experience active systemic lupus erythematosus disease. Themethod comprises, or consists essentially of, (a) determining whether ornot a mammal having systemic lupus erythematosus disease contains cellshaving an activity signature 3, and (b) classifying the mammal as beinglikely to experience the active systemic lupus erythematosus disease ifthe mammal contains the cells and classifying the mammal as not beinglikely to experience the active systemic lupus erythematosus disease ifthe mammal does not contain the cells. The mammal can be a human. Thecells can be peripheral blood mononuclear cells. The activity signature3 can comprise, or consist essentially of, at least 10, 20, 30, 40, 50,60, 70, 80, 90, 95, or 100 percent of the genes listed in Table 19.

In yet another aspect, this document features a method for identifying amammal likely to experience active systemic lupus erythematosus disease.The method comprises, or consists essentially of, (a) determiningwhether or not a mammal contains cells that express at least 2 of thegenes listed in Table 19 to an extent greater than or less than theaverage level of expression exhibited in control cells from one or morecontrol mammals, wherein the mammal and the one or more control mammalsare from the same species; and (b) classifying the mammal as beinglikely to experience the active systemic lupus erythematosus disease ifthe mammal contains the cells and classifying the mammal as not beinglikely to experience the active systemic lupus erythematosus disease ifthe mammal does not contain the cells. The mammal can be a human. Theone or more control mammals can be healthy humans. The one or morecontrol mammals can be humans with inactive systemic lupuserythematosus. The cells and the control cells can be peripheral bloodmononuclear cells. The method can include determining whether or not themammal contains cells that express at least 5 of the genes or at least10 of the genes to an extent greater than or less than the level ofexpression exhibited in the control cells. The extent can be greaterthan or less than the average level of expression exhibited in controlcells from at least 10 control mammals or from at least 20 controlmammals. The determining step can include measuring the level of mRNAexpressed from at least 2 of the genes or from at least 5 of the genes.

In yet another aspect, this document features a method for identifying amammal likely to respond to an anti-IFN treatment for systemic lupuserythematosus. The method comprises, or consists essentially of, (a)determining whether or not a mammal having systemic lupus erythematosusdisease contains cells having an IFN signature 1, and (b) classifyingthe mammal as being likely to respond to the anti-IFN treatment if themammal contains the cells and classifying the mammal as not being likelyto respond to the anti-IFN treatment if the mammal does not contain thecells. The mammal can be a human. The cells can be peripheral bloodmononuclear cells. The IFN signature 1 can comprise, or consistessentially of, at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, or 100percent of the genes listed in Table 5.

In yet another aspect, this document features a method for identifying amammal likely to respond to an anti-IFN treatment for systemic lupuserythematosus. The method comprises, or consists essentially of, (a)determining whether or not a mammal contains cells that express at least2 of the genes listed in Table 5 to an extent greater than or less thanthe average level of expression exhibited in control cells from one ormore control mammals, wherein the mammal and the one or more controlmammals are from the same species; and (b) classifying the mammal asbeing likely to respond to an anti-IFN treatment for systemic lupuserythematosus if the mammal contains the cells and classifying themammal as not being likely to respond to an anti-IFN treatment forsystemic lupus erythematosus if the mammal does not contain the cells.The mammal can be a human. The one or more control mammals can behealthy humans. The cells and the control cells can be peripheral bloodmononuclear cells. The method can include determining whether or not themammal contains cells that express at least 5 of the genes or at least10 of the genes to an extent greater than or less than the level ofexpression exhibited in the control cells. The extent can be greaterthan or less than the average level of expression exhibited in controlcells from at least 10 control mammals or from at least 20 controlmammals. The determining step can include measuring the level of mRNAexpressed from at least 2 of the genes or from at least 5 of the genes.

In yet another aspect, this document features a method for assessingeffectiveness of a treatment for systemic lupus erythematosus. Themethod comprises, or consists essentially of, determining whether or nota mammal having systemic lupus erythematosus disease and having receiveda treatment for the systemic lupus erythematosus disease contains cellshaving an IFN signature 1, an activity signature 1, an activitysignature 2, or an activity signature 3 to a level less than thatobserved prior to the treatment, wherein the presence of the cellsindicates that the treatment is effective. The mammal can be a human.The cells can be peripheral blood mononuclear cells. The IFN signature 1can comprise, or consist essentially of, at least 10, 20, 30, 40, 50,60, 70, 80, 90, 95, or 100 percent of the genes listed in Table 5. Theactivity signature 1 can comprise, or consist essentially of, at least10, 20, 30, 40, 50, 60, 70, 80, 90, 95, or 100 percent of the geneslisted in Table 16. The activity signature 2 can comprise, or consistessentially of, at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, or 100percent of the genes listed in Table 17. The activity signature 3 cancomprise, or consist essentially of, at least 10, 20, 30, 40, 50, 60,70, 80, 90, 95, or 100 percent of the genes listed in Table 19.

In yet another aspect, this document features a method for assessingeffectiveness of a treatment for systemic lupus erythematosus. Themethod comprises, or consists essentially of, determining whether or nota mammal having systemic lupus erythematosus disease and having receiveda treatment for the systemic lupus erythematosus disease contains cellsthat express at least 2 of the genes listed in Table 5, 7, 9, 16, 17, or19 to an extent greater than or less than the average level ofexpression exhibited in cells obtained from the mammal prior to thetreatment, where the presence of the cells indicates that the treatmentis effective. The mammal can be a human. The cells can be peripheralblood mononuclear cells. The method can include determining whether ornot the mammal contains cells that express at least 5 of the genes or atleast 10 of the genes to an extent greater than or less than the levelof expression exhibited in the cells obtained from the mammal prior tothe treatment. The determining step can include measuring the level ofmRNA expressed from at least 2 of the genes or from at least 5 of thegenes.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention pertains. Although methods and materialssimilar or equivalent to those described herein can be used in thepractice or testing of the present invention, suitable methods andmaterials are described below. All publications, patent applications,patents, and other references mentioned herein are incorporated byreference in their entirety. In case of conflict, the presentspecification, including definitions, will control. In addition, thematerials, methods, and examples are illustrative only and not intendedto be limiting.

Other features and advantages of the invention will be apparent from thefollowing detailed description, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a graph plotting the IFN scores that were calculated for SLEpatients and control subjects using the normalized expression levels ofthe 14 IFN-regulated genes that comprise the IFN signature; p=2.8×10⁻⁷.

FIG. 2 is a graph plotting the number of SLE criteria observed in the 24SLE patients with the highest IFN scores and in the 24 SLE patients withthe lowest IFN scores; p=0.002.

FIG. 3 is a graph plotting the number of SLE criteria met by eachpatient against the IFN score of each patient.

FIG. 4 is a bar graph showing the percent of patients in the IFN-highand IFN-low groups with ACR-defined criteria for renal and/or CNSdisease (p=7.7×10⁻⁶) or hematologic involvement (p=6.1×10⁻⁹).

FIGS. 5A and 5B are graphs showing the percentage of SLE patients thatexhibit particular clinical features, correlated with patient subgroup.FIG. 5A shows the percentage of patients exhibiting the indicatedclinical features in group 3 (ribosomal/mitochondrial positive, IFNpositive, nuclear/transcription negative) vs. all other groups. FIG. 5Bshows the percentage of patients exhibiting the indicated clinicalfeatures in the indicated combinations of groups vs. all other groups.CVA, cerebrovascular accident. LFT, liver function test.

FIG. 6A is a summary of the signatures defining the four SLE subgroupsdescribed herein. IFN, interferon; R/M, ribosomal/mitochondrial; N/T,nuclear/transcription. FIGS. 6B, 6C, 6D, and 6E are graphs showingclinical features associated with a subset of IFN signature positivepatients. The frequency of selected clinical manifestations in each SLEsubgroup is presented as the percentage of patients in the indicatedsubgroup. P-values were derived from a chi-square test comparing thefrequency in the indicated subgroup vs. the frequency in all othersubgroups combined. For FIG. 6E, the p-value represents comparison ofgroups 0 and 2 combined vs. all other patients.

FIG. 7 is a series of graphs showing the spectrum of clinical featuresin the SLE cohort. Clinical data for the initial visits of 81 patientsanalyzed by microarray (left panels) and their 404 follow-up visits(right panels) are summarized. Shown are disease activity as measured bySLEDAI (A) and PGA (B), the frequency of selected clinical features (C),and use of specific medications (D).

DETAILED DESCRIPTION

This document provides methods and materials involved in diagnosing SLEsuch as methods and materials involved in diagnosing SLE, diagnosingsevere SLE, and assessing a mammal's susceptibility to develop severeSLE. For example, this document provides nucleic acid arrays that can beused to diagnose SLE, severe SLE, and/or SLE-AIP in a mammal. Sucharrays can allow clinicians to diagnose SLE, severe SLE, and/or SLE-AIPbased on a determination of the expression levels of many genes that aredifferentially expressed. In addition, the methods and materialsprovided herein can be used to assess SLE activity, determine thelikelihood of experiencing active SLE, and detect SLE treatmenteffectiveness.

1. Diagnosing SLE

This document provides methods for diagnosing a mammal (e.g., a human)as having SLE. In one embodiment, a mammal can be diagnosed as havingSLE if it is determined that the mammal contains cells that express oneor more of the genes listed in Table 1 or Tables 5, 7, 8, and 9 at alevel that is greater or less than the average level of expression ofthe same one or more genes observed in control cells obtained fromcontrol mammals. In another embodiment, a mammal can be diagnosed ashaving SLE if it is determined that the mammal contains cells thatexpress one or more of the genes listed in Table 2 or in Table 7 at alevel that is greater than the average level of expression of the sameone or more genes observed in control cells obtained from controlmammals. In yet another embodiment, a mammal can be diagnosed as havingSLE if it is determined that the mammal contains cells that express oneor more of the genes listed in Table 3 or Table 8 at a level that isless than the average level of expression of the same one or more genesobserved in control cells obtained from control mammals.

The mammal can be any mammal such as a human, dog, mouse, or rat. Anycell type can be isolated and evaluated. For example, peripheral bloodmononuclear cells (PMBC), total white blood cells, lymph node cells,spleen cells, or tonsil cells can be isolated from a human patient andevaluated to determine if that patient contains cells that (1) expressone or more of the genes listed in Table 1 or Tables 5, 7, 8, and 9 at alevel that is greater or less than the average level of expressionobserved in control cells, (2) express one or more of the genes listedin Table 2 or in Table 7 at a level that is greater than the averagelevel of expression observed in control cells, or (3) express one ormore of the genes listed in Table 3 or Table 8 at a level that is lessthan the average level of expression observed in control cells. Theexpression of any number of the genes listed in Tables 1, 2, 3, 5, 7, 8,or 9 can be evaluated to diagnose SLE. For example, the expression ofone or more than one (e.g., two, three, four, five, six, seven, eight,nine, ten, 15, 20, 25, 30, or more than 30) of the genes listed in Table1, 2, 3, 5, 7, 8, or 9 can be used. Examples of gene combinations thatcan be evaluated include, without limitation, SP100 and F1111000;N1-acetyltransferase and RPS10; RPL39 and COX6A1; RPS3A, ATP5L andTIMM10; KIAA0471 and SFRS protein kinase 2; metallothionein 1F, COX7C,RPL9, and KIAA0876 protein; and torsin B, STAT1, UQCR, and IL6R.

The expression level can be greater than or less than the average levelobserved in control cells obtained from control mammals. Typically, agene can be classified as being expressed at a level that is greaterthan or less than the average level observed in control cells if theexpression levels differ by at least 1-fold (e.g., 1.5-fold, 2-fold,3-fold, or more than 3-fold). In addition, the control cells typicallyare the same type of cells as those isolated from the mammal beingevaluated. In some cases, the control cells can be isolated from one ormore mammals that are from the same species as the mammal beingevaluated. When diagnosing SLE, the control cells can be isolated fromhealthy mammals such as healthy humans who do not have SLE. Any numberof control mammals can be used to obtain the control cells. For example,control cells can be obtained from one or more healthy mammals (e.g., atleast 5, at least 10, at least 15, at least 20, or more than 20 controlmammals).

Any method can be used to determine whether or not a specific gene isexpressed at a level that is greater or less than the average level ofexpression observed in control cells. For example, the level ofexpression from a particular gene can be measured by assessing the levelof mRNA expression from the gene. Levels of mRNA expression can beevaluated using, without limitation, northern blotting, slot blotting,quantitative reverse transcriptase polymerase chain reaction (RT-PCR),or chip hybridization techniques. Methods for chip hybridization assaysinclude, without limitation, those described herein. Such methods can beused to determine simultaneously the relative expression levels ofmultiple mRNAs. Alternatively, the level of expression from a particulargene can be measured by assessing polypeptide levels. Polypeptide levelscan be measured using any method such as immuno-based assays (e.g.,ELISA), western blotting, protein arrays, or silver staining

TABLE 1 Genes with expression levels that differ between SLE patientsand normal controls Accession No. Gene U60060 fasciculation andelongation protein zeta 1 (zygin I) AF057036 collagen-like tail subunit(single strand of homotrimer) of asymmetric acetylcholinesterase M931073-hydroxybutyrate dehydrogenase (heart, mitochondrial) U14575 proteinphosphatase 1, regulatory (inhibitor) subunit 8 X15882 collagen VIalpha-2 C-terminal globular domain S68805 glycine amidinotransferase(L-arginine: glycine amidinotransferase) U75744 deoxyribonuclease I-like3 AF091071 similar to S. cerevisiae RER1 AI651806 cysteine-rich motorneuron 1 AB028994 KIAA1071 protein S75168 megakaryocyte-associatedtyrosine kinase X73617 T cell receptor delta locus X07730 kallikrein 3,(prostate specific antigen) AF009787 T cell receptor beta locus M21624 Tcell receptor delta locus AB009598 beta-1,3-glucuronyltransferase 3(glucuronosyltransferase I) AL021154 E2F transcription factor 2 L25444TAF6 RNA polymerase II, TATA box binding protein (TBP)-associatedfactor, 80 kD AJ001383 lymphocyte antigen 94 homolog, activatingNK-receptor; NK-p46, (mouse) U75370 polymerase (RNA) mitochondrial (DNAdirected) AL049365 DKFZp586A0618 M16801 nuclear receptor subfamily 3,group C, member 2 M28827 CD1C antigen, c polypeptide U51712 hypotheticalprotein SMAP31 X66079 Spi-B transcription factor (Spi-1/PU.1 related)U11276 killer cell lectin-like receptor subfamily B, member 1 M36881lymphocyte-specific protein tyrosine kinase M31523 transcription factor3 (E2A immunoglobulin enhancer binding factors E12/E47) M26062interleukin 2 receptor, beta AF026031 putative mitochondrial outermembrane protein import receptor AB011115 KIAA0543 protein AF041261leukocyte immunoglobulin-like receptor, subfamily A (without TM domain),member 4 D55716 MCM7 minichromosome maintenance deficient 7 (S.cerevisiae) L04282 zinc finger protein 148 (pHZ-52) AJ001687 DNA segmenton chromosome 12 (unique) 2489 expressed sequence AI524873 like mousebrain protein E46 U76421 adenosine deaminase, RNA-specific, B1 (homologof rat RED1) AF031137 lymphocyte antigen 117 X59871 transcription factor7 (T-cell specific, HMG-box) U43408 tyrosine kinase, non-receptor, 1AB018289 KIAA0746 protein AI761647 IMAGE-2370113 M18737 granzyme A(granzyme 1, cytotoxic T-lymphocyte-associated serine esterase 3)AB023220 ubiquitin specific protease 20 W26633 melanoma antigen, familyD, 1 M68892 integrin, beta 7 AJ236885 zinc finger protein 148 (pHZ-52)L13858 son of sevenless (Drosophila) homolog 2 AF094481 CGG tripletrepeat binding protein 1 M28215 RAB5A, member RAS oncogene family U43083guanine nucleotide binding protein (G protein), q polypeptide X02344tubulin, beta, 2 M22324 alanyl (membrane) aminopeptidase (aminopeptidaseN, aminopeptidase M, microsomal aminopeptidase, CD13, p150) Y07566Ric-like, expressed in many tissues (Drosophila) U50553 DEAD/H(Asp-Glu-Ala-Asp/His) box polypeptide 3 X54134 protein tyrosinephosphatase, receptor type, E L40388 thyroid receptor interactingprotein 15 L19872 aryl hydrocarbon receptor U78107N-ethylmaleimide-sensitive factor attachment protein, gamma AL050272DKFZP566B183 protein U56998 cytokine-inducible kinase AI189226 RAB31,member RAS oncogene family Z50781 delta sleep inducing peptide,immunoreactor S87759 protein phosphatase 1A (formerly 2C),magnesium-dependent, alpha isoform U88629 ELL-RELATED RNA POLYMERASE II,ELONGATION FACTOR AF006513 chromodomain helicase DNA binding protein 1AI138605 hypothetical protein DKFZp566A1524 L16794 MADS boxtranscription enhancer factor 2, polypeptide D (myocyte enhancer factor2D) AL080235 Ras-induced senescence 1 L17418 complement component(3b/4b) receptor 1, including Knops blood group system Y00816 complementcomponent (3b/4b) receptor 1, including Knops blood group system M63835Fc fragment of IgG, high affinity Ia, receptor for (CD64) L13943glycerol kinase U89278 early development regulator 2 (homolog ofpolyhomeotic 2) U58334 tumor protein p53 binding protein, 2 X54134protein tyrosine phosphatase, receptor type, E X59834 glutamate-ammonialigase (glutamine synthase) AL047596 capicua homolog (Drosophila)AB023211 peptidyl arginine deiminase, type II D43945 transcriptionfactor EC U79273 clone 23933 Z18956 solute carrier family 6(neurotransmitter transporter, taurine), member 6 Y10313interferon-related developmental regulator 1 AF004849 homeodomaininteracting protein kinase 3 AI808958 KIAA0870 protein U47634 tubulin,beta, 4 X55988 ribonuclease, RNase A family, 2 (liver,eosinophil-derived neurotoxin) W29030 CGI-49 protein U12471thrombospondin-1 AF013591 sudD (suppressor of bimD6, Aspergillusnidulans) homolog X52015 interleukin 1 receptor antagonist M16967coagulation factor V (proaccelerin, labile factor) U57094 RAB27A, memberRAS oncogene family U66711 lymphocyte antigen 6 complex, locus EAA521060 IMAGE-826408 X68090 IgG Fc receptor class IIA Y08136 acidsphingomyelinase-like phosphodiesterase AL049685 hypothetical proteinsimilar to small G proteins, especially RAP-2A L28957 phosphatecytidylyltransferase 1, choline, alpha isoform Z22576 CD69 antigen (p60,early T-cell activation antigen) U41766 a disintegrin andmetalloproteinase domain 9 (meltrin gamma) M57230 interleukin 6 signaltransducer (gp130, oncostatin M receptor) X17094 paired basic amino acidcleaving enzyme (furin, membrane associated receptor protein) AC005192interferon-related developmental regulator 1 AI547258 metallothionein 2AL22075 guanine nucleotide binding protein (G protein), alpha 13 U22431hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helixtranscription factor) AB006746 phospholipid scramblase 1 AF030196stannin AA010078 H4 histone family, member D X56807 desmocollin 2AL080156 DKFZP434J214 protein AF017257 v-ets erythroblastosis virus E26oncogene homolog 2 (avian) AL049340 DKFZp564P056 M24283 intercellularadhesion molecule 1 (CD54), human rhinovirus receptor D498176-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 AF016903 agrinU77914 jagged 1 (Alagille syndrome) M33882 myxovirus (influenza)resistance 1, homolog of murine (interferon- inducible protein p78)U68385 Meis1, myeloid ecotropic viral integration site 1 homolog 3(mouse) L05515 cAMP response element-binding protein CRE-BPa U15555serine palmitoyltransferase, long chain base subunit 2 L42025 HIV-1 Revbinding protein X07834 superoxide dismutase 2, mitochondrial D90144small inducible cytokine A3 M13755 interferon-stimulated protein, 15 kDaM83670 carbonic anhydrase IV M55047 synaptotagmin I U91512 ninjurin 1AB008775 aquaporin 9 X79535 tubulin, beta polypeptide J04102 v-etserythroblastosis virus E26 oncogene homolog 2 (avian) D10040fatty-acid-Coenzyme A ligase, long-chain 2 AW044649 sin3-associatedpolypeptide, 30 kD X03473 H1 histone family, member 0 AB007448 solutecarrier family 22 (organic cation transporter), member 4 Z14138mitogen-activated protein kinase kinase kinase 8 X02419 uPA U10473UDP-Gal: betaGlcNAc beta 1,4-galactosyltransferase, polypeptide 1AI679353 solute carrier family 11 (proton-coupled divalent metal iontransporters), member 1 AA203213 interferon-stimulated protein, 15 kDaAB018259 KIAA0716 gene product AF055993 sin3-associated polypeptide, 30kD X54486 serine (or cysteine) proteinase inhibitor, clade G (C1inhibitor), member 1 AJ225089 2′-5′-oligoadenylate synthetase-likeAL022318 similar to APOBEC1 S59049 regulator of G-protein signalling 1Y10032 serum/glucocorticoid regulated kinase AI924594 tetraspan 2 D21205zinc finger protein 147 (estrogen-responsive finger protein) U37707membrane protein, palmitoylated 3 (MAGUK p55 subfamily member 3) L403872′-5′-oligoadenylate synthetase-like X78711 glycerol kinase D10923putative chemokine receptor; GTP-binding protein AW006742 IMAGE-2489058AL109730 EUROIMAGE 68600 X99699 XIAP associated factor-1 AB000115hypothetical protein, expressed in osteoblast L13210 lectin,galactoside-binding, soluble, 3 binding protein U22970 interferon,alpha-inducible protein (clone IFI-6-16) U96721 Hermansky-Pudlaksyndrome L10126 activin A receptor, type IB S62138 TLS/CHOP M33684protein tyrosine phosphatase, non-receptor type 1 M63978 vascularendothelial growth factor X89101 tumor necrosis factor receptorsuperfamily, member 6 M60278 diphtheria toxin receptor (heparin-bindingepidermal growth factor-like growth factor) X59770 interleukin 1receptor, type II X04500 interleukin 1, beta D30783 epiregulin U43774 Fcfragment of IgA, receptor for

TABLE 2 Genes from Table 1 that are higher in SLE patients as comparedto controls Accession No. Gene L13858 son of sevenless (Drosophilia)homolog 2 AF094481 CGG triplet repeat binding protein 1 M28215 RAB5A,member RAS oncogene family U43083 guanine nucleotide binding protein (Gprotein), q polypeptide X02344 tubulin, beta, 2 M22324 alanyl (membrane)aminopeptidase (aminopeptidase N, aminopeptidase M, microsomalaminopeptidase, CD13, p150) Y07566 Ric-like, expressed in many tissues(Drosophila) U50553 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 3X54134 protein tyrosine phosphatase, receptor type, E L40388 thyroidreceptor interacting protein 15 L19872 aryl hydrocarbon receptor U78107N-ethylmaleimide-sensitive factor attachment protein, gamma AL050272DKFZP566B183 protein U56998 cytokine-inducible kinase AI189226 RAB31,member RAS oncogene family Z50781 delta sleep inducing peptide,immunoreactor S87759 protein phosphatase 1A (formerly 2C),magnesium-dependent, alpha isoform U88629 ELL-RELATED RNA POLYMERASE II,ELONGATION FACTOR AF006513 chromodomain helicase DNA binding protein 1AI138605 hypothetical protein DKFZp566A1524 L16794 MADS boxtranscription enhancer factor 2, polypeptide D (myocyte enhancer factor2D) AL080235 Ras-induced senescence 1 L17418 complement component(3b/4b) receptor 1, including Knops blood group system Y00816 complementcomponent (3b/4b) receptor 1, including Knops blood group system M63835Fc fragment of IgG, high affinity Ia, receptor for (CD64) L13943glycerol kinase U89278 early development regulator 2 (homolog ofpolyhomeotic 2) U58334 tumor protein p53 binding protein, 2 X54134protein tyrosine phosphatase, receptor type, E X59834 glutamate-ammonialigase (glutamine synthase) AL047596 capicua homolog (Drosophila)AB023211 peptidyl arginine deiminase, type II D43945 transcriptionfactor EC U79273 clone 23933 Z18956 solute carrier family 6(neurotransmitter transporter, taurine), member 6 Y10313interferon-related developmental regulator 1 AF004849 homeodomaininteracting protein kinase 3 AI808958 KIAA0870 protein U47634 tubulin,beta, 4 X55988 ribonuclease, RNase A family, 2 (liver,eosinophil-derived neurotoxin) W29030 CGI-49 protein U12471thrombospondin-1 AF013591 sudD (suppressor of bimD6, Aspergillusnidulans) homolog X52015 interleukin 1 receptor antagonist M16967coagulation factor V (proaccelerin, labile factor) U57094 RAB27A, memberRAS oncogene family U66711 lymphocyte antigen 6 complex, locus EAA521060 IMAGE-826408 X68090 IgG Fc receptor class IIA Y08136 acidsphingomyelinase-like phosphodiesterase AL049685 hypothetical proteinsimilar to small G proteins, especially RAP-2A L28957 phosphatecytidylyltransferase 1, choline, alpha isoform Z22576 CD69 antigen (p60,early T-cell activation antigen) U41766 a disintegrin andmetalloproteinase domain 9 (meltrin gamma) M57230 interleukin 6 signaltransducer (gp130, oncostatin M receptor) X17094 paired basic amino acidcleaving enzyme (furin, membrane associated receptor protein) AC005192interferon-related developmental regulator 1 AI547258 metallothionein 2AL22075 guanine nucleotide binding protein (G protein), alpha 13 U22431hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helixtranscription factor) AB006746 phospholipid scramblase 1 AF030196stannin AA010078 H4 histone family, member D X56807 desmocollin 2AL080156 DKFZP434J214 protein AF017257 v-ets erythroblastosis virus E26oncogene homolog 2 (avian) AL049340 DKFZp564P056 M24283 intercellularadhesion molecule 1 (CD54), human rhinovirus receptor D498176-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 AF016903 agrinU77914 jagged 1 (Alagille syndrome) M33882 myxovirus (influenza)resistance 1, homolog of murine (interferon- inducible protein p78)U68385 Meis1, myeloid ecotropic viral integration site 1 homolog 3(mouse) L05515 cAMP response element-binding protein CRE-BPa U15555serine palmitoyltransferase, long chain base subunit 2 L42025 HIV-1 Revbinding protein X07834 superoxide dismutase 2, mitochondrial D90144small inducible cytokine A3 M13755 interferon-stimulated protein, 15 kDaM83670 carbonic anhydrase IV M55047 synaptotagmin I U91512 ninjurin 1AB008775 aquaporin 9 X79535 tubulin, beta polypeptide J04102 v-etserythroblastosis virus E26 oncogene homolog 2 (avian) D10040fatty-acid-Coenzyme A ligase, long-chain 2 AW044649 sin3-associatedpolypeptide, 30 kD X03473 H1 histone family, member 0 AB007448 solutecarrier family 22 (organic cation transporter), member 4 Z14138mitogen-activated protein kinase kinase kinase 8 X02419 uPA U10473UDP-Gal: betaGlcNAc beta 1,4-galactosyltransferase, polypeptide 1AI679353 solute carrier family 11 (proton-coupled divalent metal iontransporters), member 1 AA203213 interferon-stimulated protein, 15 kDaAB018259 KIAA0716 gene product AF055993 sin3-associated polypeptide, 30kD X54486 serine (or cysteine) proteinase inhibitor, clade G (C1inhibitor), member 1 AJ225089 2′-5′-oligoadenylate synthetase-likeAL022318 similar to APOBEC1 S59049 regulator of G-protein signalling 1Y10032 serum/glucocorticoid regulated kinase AI924594 tetraspan 2 D21205zinc finger protein 147 (estrogen-responsive finger protein) U37707membrane protein, palmitoylated 3 (MAGUK p55 subfamily member 3) L403872′-5′-oligoadenylate synthetase-like X78711 glycerol kinase D10923putative chemokine receptor; GTP-binding protein AW006742 IMAGE-2489058AL109730 EUROIMAGE 68600 X99699 XIAP associated factor-1 AB000115hypothetical protein, expressed in osteoblast L13210 lectin,galactoside-binding, soluble, 3 binding protein U22970 interferon,alpha-inducible protein (clone IFI-6-16) U96721 Hermansky-Pudlaksyndrome L10126 activin A receptor, type IB S62138 TLS/CHOP M33684protein tyrosine phosphatase, non-receptor type 1 M63978 vascularendothelial growth factor X89101 tumor necrosis factor receptorsuperfamily, member 6 M60278 diphtheria toxin receptor (heparin-bindingepidermal growth factor-like growth factor) X59770 interleukin 1receptor, type II X04500 interleukin 1, beta D30783 epiregulin U43774 Fcfragment of IgA, receptor for

TABLE 3 Genes from Table 1 that are lower in SLE patients as compared tocontrols Accession No. Gene U60060 fasciculation and elongation proteinzeta 1 (zygin I) AF057036 collagen-like tail subunit (single strand ofhomotrimer) of asymmetric acetylcholinesterase M93107 3-hydroxybutyratedehydrogenase (heart, mitochondrial) U14575 protein phosphatase 1,regulatory (inhibitor) subunit 8 X15882 collagen VI alpha-2 C-terminalglobular domain S68805 glycine amidinotransferase (L-arginine:glycineamidinotransferase) U75744 deoxyribonuclease I-like 3 AF091071 similarto S. cerevisiae RER1 AI651806 cysteine-rich motor neuron 1 AB028994KIAA1071 protein S75168 megakaryocyte-associated tyrosine kinase X73617T cell receptor delta locus X07730 kallikrein 3, (prostate specificantigen) AF009787 T cell receptor beta locus M21624 T cell receptordelta locus AB009598 beta-1,3-glucuronyltransferase 3(glucuronosyltransferase I) AL021154 E2F transcription factor 2 L25444TAF6 RNA polymerase II, TATA box binding protein (TBP)- associatedfactor, 80 kD AJ001383 lymphocyte antigen 94 homolog, activatingNK-receptor; NK-p46, (mouse) U75370 polymerase (RNA) mitochondrial (DNAdirected) AL049365 DKFZp586A0618 M16801 nuclear receptor subfamily 3,group C, member 2 M28827 CD1C antigen, c polypeptide U51712 hypotheticalprotein SMAP31 X66079 Spi-B transcription factor (Spi-1/PU.1 related)U11276 killer cell lectin-like receptor subfamily B, member 1 M36881lymphocyte-specific protein tyrosine kinase M31523 transcription factor3 (E2A immunoglobulin enhancer binding factors E12/E47) M26062interleukin 2 receptor, beta AF026031 putative mitochondrial outermembrane protein import receptor AB011115 KIAA0543 protein AF041261leukocyte immunoglobulin-like receptor, subfamily A (without TM domain),member 4 D55716 MCM7 minichromosome maintenance deficient 7 (S.cerevisiae) L04282 zinc finger protein 148 (pHZ-52) AJ001687 DNA segmenton chromosome 12 (unique) 2489 expressed sequence AI524873 like mousebrain protein E46 U76421 adenosine deaminase, RNA-specific, B1 (homologof rat RED1) AF031137 lymphocyte antigen 117 X59871 transcription factor7 (T-cell specific, HMG-box) U43408 tyrosine kinase, non-receptor, 1AB018289 KIAA0746 protein AI761647 IMAGE-2370113 M18737 granzyme A(granzyme 1, cytotoxic T-lymphocyte-associated serine esterase 3)AB023220 ubiquitin specific protease 20 W26633 melanoma antigen, familyD, 1 M68892 integrin, beta 7 AJ236885 zinc finger protein 148 (pHZ-52)

2. Diagnosing Severe SLE and SLE-AIP

This document also provides methods for diagnosing a mammal (e.g., ahuman) as having severe SLE or SLE-AIP. In one embodiment, a mammal canbe diagnosed as having severe SLE or SLE-AIP if it is determined thatthe mammal contains cells that express one or more of the genes listedin Table 4 or Table 5 at a level that is greater than or less than theaverage level of expression of the same one or more genes observed incontrol cells obtained from control mammals.

As described herein, the mammal can be any mammal such as a human, dog,mouse, or rat. Any cell type can be isolated and evaluated. For example,peripheral blood mononuclear cells (PMBC), total white blood cells,lymph node cells, spleen cells, or tonsil cells can be isolated from ahuman patient and evaluated to determine if that patient contains cellsthat express one or more of the genes listed in Table 4 or Table 5 at alevel that is greater than or less than the average level of expressionobserved in control cells. The expression of any number of the geneslisted in Table 4 or Table 5 can be evaluated to diagnose severe SLE orSLE-AIP. For example, the expression of one or more than one (e.g., two,three, four, five, six, seven, eight, nine, ten, 11, 12, 13, or all 14)of the genes listed in Table 4 or Table 5 can be used. Examples of genecombinations that can be evaluated include, without limitation,biliverdin reductase A and metallothionein 2A; 2′-5′-OAS2 and SCO2;IFIT-3, IFN regulatory factor 7, and RNA helicase; leucineaminopeptidase, metallothionein 1E, and biliary glycoprotein; andAW474434, UBE2L6, IFIT-1, MX2, and hypothetical AL031602.

The expression level can be greater than or less than the average levelobserved in control cells obtained from control mammals. Typically, agene can be classified as being expressed at a level that is greaterthan or less than the average level observed in control cells if theexpression levels differ by at least 1-fold (e.g., 1.5-fold, 2-fold,3-fold, or more than 3-fold). In addition, the control cells typicallyare the same type of cells as those isolated from the mammal beingevaluated. In some cases, the control cells can be isolated from one ormore mammals that are from the same species as the mammal beingevaluated. When diagnosing severe SLE or SLE-AIP, the control cells canbe isolated from mammals having mild SLE or from healthy mammals such ashealthy humans who do not have SLE. Any number of control mammals can beused to obtain the control cells. For example, control cells can beobtained from one or more healthy mammals (e.g., at least 5, at least10, at least 15, at least 20, or more than 20 control mammals).

TABLE 4 Genes with expression levels that differ between SLE patientshaving low and high IFN scores Accession No. Gene M63835 Fc fragment ofIgG, high affinity Ia, receptor for (CD64) X54486 serine (or cysteine)proteinase inhibitor, clade G (C1 inhibitor), member 1 L13210 lectin,galactoside-binding, soluble, 3 binding protein M33882 myxovirus(influenza) resistance 1, homolog of murine (interferon-inducibleprotein p78) AA203213 interferon-stimulated protein, 15 kDa X99699 XIAPassociated factor-1 AJ225089 2′-5′-oligoadenylate synthetase-like U22970interferon, alpha-inducible protein (clone IFI-6-16) AB000115Interferon-induced protein 44-like (hypothetical protein, expressed inosteoblast) AL047596 capicua homolog (Drosophila) AB006746 phospholipidscramblase 1 AL022318 APOBEC3B (similar to APOBEC1) U66711 lymphocyteantigen 6 complex, locus E X55988 ribonuclease, RNase A family, 2(liver, eosinophil-derived neurotoxin)

Any method can be used to determine whether or not a specific gene isexpressed at a level that is greater or less than the average level ofexpression observed in control cells. For example, the level ofexpression from a particular gene can be measured by assessing the levelof mRNA expression from the gene. Levels of mRNA expression can beevaluated using, without limitation, northern blotting, slot blotting,quantitative reverse transcriptase polymerase chain reaction (RT-PCR),or chip hybridization techniques. Methods for chip hybridization assaysinclude, without limitation, those described herein. Such methods can beused to determine simultaneously the relative expression levels ofmultiple mRNAs. Alternatively, the level of expression from a particulargene can be measured by assessing polypeptide levels. Polypeptide levelscan be measured using any method such as immuno-based assays (e.g.,ELISA), western blotting, or silver staining

3. Identifying Mammals Predisposed to Develop Severe SLE and SLE-AIP

This document also provides methods for diagnosing a mammal (e.g., ahuman) as being predisposed to develop severe SLE or SLE-AIP. In oneembodiment, a mammal can be diagnosed as being predisposed to developsevere SLE or SLE-AIP if it is determined that the mammal contains cellsthat express one or more of the genes listed in Table 4 or Table 5 at alevel that is greater than or less than the average level of expressionof the same one or more genes observed in control cells obtained fromcontrol mammals.

As described herein, the mammal can be any mammal such as a human, dog,mouse, or rat. Any cell type can be isolated and evaluated. For example,peripheral blood mononuclear cells (PMBC), total white blood cells,lymph node cells, spleen cells, or tonsil cells can be isolated from ahuman patient and evaluated to determine if that patient contains cellsthat express one or more of the genes listed in Table 4 or Table 5 at alevel that is greater than the average level of expression observed incontrol cells. The expression of any number of the genes listed in Table4 or Table 5 can be evaluated to diagnose a mammal as being predisposedto develop severe SLE or SLE-AIP. For example, the expression of one ormore than one (e.g., two, three, four, five, six, seven, eight, nine,ten, 11, 12, 13, or all 14) of the genes listed in Table 4 or Table 5can be used. Examples of gene combinations that can be evaluatedinclude, without limitation, those disclosed herein.

The expression level can be greater than or less than the average levelobserved in control cells obtained from control mammals. Typically, agene can be classified as being expressed at a level that is greaterthan or less than the average level observed in control cells if theexpression levels differ by at least 1-fold (e.g., 1.5-fold, 2-fold,3-fold, or more than 3-fold). In addition, the control cells typicallyare the same type of cells as those isolated from the mammal beingevaluated. In some cases, the control cells can be isolated from one ormore mammals that are from the same species as the mammal beingevaluated. When determining a mammal's susceptibility to develop severeSLE or SLE-AIP, the control cells can be isolated from mammals havingmild SLE or from healthy mammals such as healthy humans who do not haveSLE. Any number of control mammals can be used to obtain the controlcells. For example, control cells can be obtained from one or morehealthy mammals (e.g., at least 5, at least 10, at least 15, at least20, or more than 20 control mammals).

Any method can be used to determine whether or not a specific gene isexpressed at a level that is greater or less than the average level ofexpression observed in control cells. For example, the level ofexpression from a particular gene can be measured by assessing the levelof mRNA expression from the gene. Levels of mRNA expression can beevaluated using, without limitation, northern blotting, slot blotting,quantitative reverse transcriptase polymerase chain reaction (RT-PCR),or chip hybridization techniques. Methods for chip hybridization assaysinclude, without limitation, those described herein. Such methods can beused to determine simultaneously the relative expression levels ofmultiple mRNAs. Alternatively, the level of expression from a particulargene can be measured by assessing polypeptide levels. Polypeptide levelscan be measured using any method such as immuno-based assays (e.g.,ELISA), western blotting, or silver staining

4. Diagnosing SLE Disease Activity

This document also provides methods and materials for diagnosing amammal (e.g., a human) as having SLE disease activity. A number ofmeasures can typically be used to define active SLE disease. Suchdisease activity measures include, without limitation, the SLE DiseaseActivity Index (SLEDAI), a physician's global assessment (PGA), theSystemic Lupus Activity Measure (SLAM), the erythrocyte sedimentationrate (ESR), the white blood cell (WBC) count, and the hematocrit. Amammal can be diagnosed as having active or inactive SLE disease basedon one or more disease activity measures. For example, a human having aPGA ≧1.5 and SLEDAI ≧3 can be diagnosed as having active SLE disease. Insome cases, a human having a PGA ≦1 and SLEDAI ≦2 can be diagnosed ashaving inactive SLE disease.

In some embodiments, a mammal can be diagnosed as having active SLEdisease if it is determined that the mammal contains cells that expressone or more of the genes listed in Table 16, Table 17, or Table 19 at alevel that is greater than or less than the average level of expressionof the same one or more genes observed in control cells obtained fromcontrol mammals.

As described herein, the mammal can be any mammal, such as a human, dog,mouse, or rat. Any cell type can be isolated and evaluated. For example,peripheral blood mononuclear cells (PBMC), total white blood cells,lymph node cells, spleen cells, or tonsil cells can be isolated from ahuman patient and evaluated to determine if that patient contains cellsthat express one or more of the genes listed in Table 16, Table 17, orTable 19 at a level that is greater than or less than the average levelof expression observed in control cells. The expression of any number ofthe genes listed in Table 16, Table 17, or Table 19 can be evaluated todiagnose SLE disease activity. For example, the expression of one ormore than one (e.g., two, three, four, five, six, seven, eight, nine,ten, 25, 37, 50, 75, 100, 156, or all) of the genes listed in Table 16,Table 17, or Table 19 can be used. Examples of gene combinations thatcan be evaluated include, without limitation, Ig kappa constant, Iglambda joining 3, thioredoxin domain containing 5, and interferoninduced transmembrane protein 1; IgM VDJ-region, Ig lambda variable3-21, Ig heavy constant mu, biliverdin reductase A, and CTD smallphosphatase-li; and signal-transducing adaptor protein-2, motilin, andinterferon-stimulated transcription factor 3, gamma 48 kDa.

The expression level can be greater than or less than the average levelobserved in control cells obtained from control mammals. Typically, agene can be classified as being expressed at a level that is greaterthan or less than the average level observed in control cells if theexpression levels differ by at least 1-fold (e.g., 1.5-fold, 2-fold,3-fold, or more than 3-fold). In addition, the control cells typicallyare the same type of cells as those isolated from the mammal beingevaluated. In some cases, the control cells can be isolated from one ormore mammals that are from the same species as the mammal beingevaluated. When diagnosing active SLE disease, the control cells can beisolated from mammals having inactive SLE or from healthy mammals, suchas healthy humans who do not have SLE. Any number of control mammals canbe used to obtain the control cells. For example, control cells can beobtained from one or more healthy mammals (e.g., at least 5, at least10, at least 15, at least 20, or more than 20 control mammals).

Any method can be used to determine whether or not a specific gene isexpressed at a level that is greater or less than the average level ofexpression observed in control cells. For example, the level ofexpression from a particular gene can be measured by assessing the levelof mRNA expression from the gene. Levels of mRNA expression can beevaluated using, without limitation, real-time quantitative PCR,northern blotting, slot blotting, or microarray technology. Methods formicroarray assays include, without limitation, those described herein.Such methods can be used to determine simultaneously the relativeexpression levels of multiple mRNAs. In some cases, the level ofexpression from a particular gene can be measured by assessingpolypeptide levels. Polypeptide levels can be measured using any methodsuch as immuno-based assays (e.g., ELISA), Western blotting, or proteinarrays.

Once a mammal (e.g., a human) has been diagnosed as having active SLEdisease, the mammal can be monitored over time for an increase or adecrease in SLE disease activity. For example, a mammal can be assessedas having an increased or decreased SLE disease activity if it isdetermined that the mammal contains cells that express one or more geneslisted in Table 16, Table 17, or Table 19 at a level that is greaterthan or less than the average level of expression of the same one ormore genes observed in cells obtained previously from the same mammal. Amammal can be monitored for SLE disease activity over any period of timewith any frequency. For example, a mammal can be monitored every threemonths for one year or once a year for as long as the mammal is alive.In some cases, the SLE disease activity of a mammal can be monitoredwith a single follow-up assessment.

A mammal can also be monitored for SLE disease activity before, during,and after being treated for SLE. For example, a mammal can be monitoredfor SLE disease activity while being treated with anti-interferontherapy, hydroxychloroquinone, steroids, or immunosuppressive drugs.Monitoring a mammal for SLE disease activity during treatment of themammal for SLE can allow the effectiveness of the SLE therapy to beassessed. For example, a decrease in SLE activity during or aftertreatment with an SLE therapy compared to the SLE activity beforetreatment with an SLE therapy can indicate that the SLE therapy iseffective. Monitoring a mammal for SLE disease activity during treatmentof the mammal for SLE can also allow responders to the SLE therapy to beidentified. For example, a decrease in SLE activity in a mammal duringtreatment with an SLE therapy compared to the SLE activity in the mammalbefore treatment with the SLE therapy can indicate that the mammal is aresponder to the SLE therapy.

5. Identifying Mammals Likely to Experience SLE Disease Activity

This document also provides methods and materials for identifyingmammals (e.g., humans) that have SLE and are likely to experience SLEdisease activity. For example, future SLE disease activity in a mammalcan be predicted by determining whether or not the mammal contains cellsthat express one or more of the genes listed in Table 16, Table 17, orTable 19 at a level that is greater than or less than the average levelof expression of the same one or more genes observed in control cellsobtained from control mammals.

6. Identifying Mammals Likely to Respond to Anti-IFN Treatment

This document also provides methods and materials for identifyingmammals (e.g., humans) likely to respond to an anti-IFN SLE treatment.For example, the methods and materials provided herein can be used toidentify SLE patients with an IFN signature. Once identified, thosepatients can be treated with an anti-IFN treatment such as humanizedanti-IFN antibodies. In some cases, the effectiveness of the anti-IFNSLE treatment can be monitored as described herein.

7. Arrays

This document also provides nucleic acid arrays. The arrays providedherein can be two-dimensional arrays, and can contain at least twodifferent nucleic acid molecules (e.g., at least three, at least five,at least ten, at least 20, at least 30, at least 50, at least 100, or atleast 200 different nucleic acid molecules). Each nucleic acid moleculecan have any length. For example, each nucleic acid molecule can bebetween 10 and 250 nucleotides (e.g., between 12 and 200, 14 and 175, 15and 150, 16 and 125, 18 and 100, and 75, or 25 and 50 nucleotides) inlength. In some cases, an array can contain one or more cDNA moleculesencoding, for example, partial or entire polypeptides. In addition, eachnucleic acid molecule can have any sequence. For example, the nucleicacid molecules of the arrays provided herein can contain sequences thatare present within the genes listed in Tables 1, 2, 3, 4, 5, 7, 8, 9,16, 17, and/or 19.

Typically, at least 25% (e.g., at least 30%, at least 40%, at least 50%,at least 60%, at least 75%, at least 80%, at least 90%, at least 95%, or100%) of the nucleic acid molecules of an array provided herein containa sequence that is (1) at least 10 nucleotides (e.g., at least 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 25, or more nucleotides) in length and(2) at least about 95 percent (e.g., at least about 96, 97, 98, 99, or100) percent identical, over that length, to a sequence present within agene listed in Tables 1, 2, 3, 4, 5, 7, 8, 9, 16, 17, and/or 19. Forexample, an array can contain 100 nucleic acid molecules located inknown positions, where each of the 100 nucleic acid molecules is 100nucleotides in length while containing a sequence that is (1) 30nucleotides is length, and (2) 100 percent identical, over that 30nucleotide length, to a sequence of one of the genes listed in Table 4.Thus, a nucleic acid molecule of an array provided herein can contain asequence present within a gene listed in Tables 1, 2, 3, 4, 5, 7, 8, 9,16, 17, and/or 19 where that sequence contains one or more (e.g., one,two, three, four, or more) mismatches.

The nucleic acid arrays provided herein can contain nucleic acidmolecules attached to any suitable surface (e.g., plastic or glass). Inaddition, any method can be use to make a nucleic acid array. Forexample, spotting techniques and in situ synthesis techniques can beused to make nucleic acid arrays. Further, the methods disclosed in U.S.Pat. Nos. 5,744,305 and 5,143,854 can be used to make nucleic acidarrays.

The invention will be further described in the following examples, whichdo not limit the scope of the invention described in the claims.

EXAMPLES Example 1 Identifying Genes that can be Used to Diagnose SLE

PBMCs were collected from 48 SLE patients and 42 healthy, age- andgender-matched control individuals. All patients had physician-verifiedSLE and met at least four of the eleven ACR criteria for lupus. Theaverage age of SLE patients was 45±11 years, and the average age ofcontrols was 34±13 years. Each PBMC sample containedmonocytes/macrophages, B and T lymphocytes, and natural killer cells.

For the first 11 patients and 11 controls, poly A⁺ mRNA was extractedfrom the collected PBMC samples. Briefly, 60 mL of peripheral blood wasdrawn into a heparinized syringe. Whole blood was layered over an equalvolume of Histopaque and centrifuged at 400×g for 30 minutes at 25° C.Plasma was harvested and stored at −80° C. PBMCs were harvested andwashed twice in 1×PBS, and the mRNA was isolated using a FastTrack mRNAisolation kit (Invitrogen, Carlsbad, Calif.).

For the next 37 patients and 31 controls, total RNA was extracted fromthe collected PBMC samples. Briefly, peripheral blood was drawn into CPTtubes (Becton-Dickinson, Franklin Lakes, N.J.), and plasma and PBMCswere collected according to manufacturer's protocol. Plasma was storedat −80° C., and total RNA was isolated from PBMCs using Trizol(Gibco-BRL, Invitrogen, Carlsbad, Calif.) followed by an RNeasy cleanup(Qiagen, Valencia, Calif.).

About 5 to 10 μg of total RNA or about 100-200 ng of poly A⁺ RNA wasused to prepare biotinylated cRNA for hybridization using the standardAffymetrix protocol (Expression Analysis Technical Manual, Affymetrix,Inc., 2000). Briefly, RNA was converted to first strand cDNA using aT7-linked oligo(dT) primer (Genset, La Jolla, Calif.) followed by secondstrand synthesis (Gibco-BRL). The dscDNA was then used as template forlabeled in vitro transcription reactions using biotinylatedribonucleotides (Enzo, Farmingdale, N.Y.). Fifteen μg of each labeledcRNA was hybridized to Affymetrix U95A GeneChips (Affymetrix, SantaClara, Calif.) using standard conditions in an Affymetrix fluidicsstation.

After chip hybridization and initial data analysis, the expressionvalues for 10,260 genes represented on the chip were compared betweenSLE patients and controls using a non-paired Student's T-test.

Affymetrix Microarray Suite (MAS) 4.0 software was used to generateexpression values (referred to as an “average difference;” AD) for eachgene. Each chip was scaled to an overall intensity of 1500 to correctfor minor differences in overall chip hybridization intensity and toallow comparison between chips. A threshold of 20 AD units was assignedto any gene that was called “Absent” by MAS. In addition, any gene withan AD less than 20 was assigned this threshold. Data from U95Av1 andU95Av2 chips were aligned by discarding the 51 probe sets that were notpresent on both chips. The analysis identified 161 unique genes thatwere differentially expressed using the following criteria: p<0.001,fold-change >1.5, mean expression value difference >100 units.

Despite the use of the same oligo(dT) primer for cDNA synthesis,consistent differences between the raw AD values obtained from polyA⁺RNA and total RNA samples were noted that were not corrected by chipscaling. Furthermore, each dataset (i.e., polyA⁺ RNA and total RNA)showed similar differential gene expression between the respectivegroups of patients and controls. For example, the initial 11/11 datasetidentified a larger than expected number of interferon-regulated genes.A gene-by-gene scaling approach thus was employed so that the twodatasets could be combined and examined together. The scaling strategywas based on the assumption that the mean expression level (mean AD) ofgenes between the two control groups (total vs. polyA⁺ RNA) should beequal. For each gene, the mean of the two control groups was compared togenerate the gene-specific scaling factor. The polyA⁺ samples werecorrected by the scaling factor so that the means of the two controlgroups (total and polyA⁺) were identical. This scaled dataset then wasused for all subsequent analysis.

Identification of stress response genes: During the course of collectingand analyzing the various samples, it was determined that many genes inperipheral blood cells undergo striking stress responses followingincubation ex vivo, even during somewhat limited periods of time (i.e.,less than 1 hour). A formal experiment was designed and performed toidentify those genes that were regulated by incubation of cells ex vivo.Changes in global gene expression were examined using whole blood afterovernight shipment by a commercial carrier. This study utilized samplesfrom eight healthy control individuals. Approximately 30 mL of blood wasdrawn into four CPT tubes. PBMCs were isolated from two tubes andresuspended in RNAlater (Ambion, Austin, Tex.). RNAlater immediatelylyses the cells and protects the RNA from degradation, thus providing anaccurate profile of gene expression immediately ex vivo. The RNApreserved in RNAlater and the two CPT tubes with whole blood wereshipped by overnight carrier. Total RNA was extracted and prepared forhybridization as described above. Thus, global gene expression profileswere obtained from both a fresh blood sample and from blood shippedovernight, with both samples coming from the same blood draw.

Data were analyzed using MAS 4.0 and each chip was scaled to 1500.Absent and low expression values were assigned a threshold of 20 ADunits as described above. A paired T-Test was used to compare the geneexpression profiles of fresh blood vs. blood shipped by overnightcarrier. Based on this experiment, 2076 genes were identified thatdisplayed significant changes in expression under these environmentalstresses (p<0.01). These genes, many of which are involved in variouscell stress pathways, were excluded from further analysis due to thehigh level of variability that they exhibited.

Comparison analyses: The individual gene expression levels of SLEpatients and controls were compared using an unpaired Student's T-test.Genes selected for further analysis met the following three criteria:

(i) p<0.001 by unpaired T-test,

(ii) change in expression of at least 1.5-fold when comparing the meansof the two groups, and

(iii) difference in expression of at least 100 when comparing the meansof the two groups.

Overall, 484 genes were differentially expressed at the p<0.001 level,while 178 genes were both differentially expressed at the p<0.001 leveland showed mean AD values that differed by more than 1.5-fold. The finaldataset of 161 individual genes (represented by 171 Genbank accessionnumbers) met all three criteria. These genes, which demonstrateddifferential expression between SLE patients and normal controls, arelisted in Table 1.

Expression values for each of the 161 genes were converted to“fold-differences” by dividing each value by the mean of the controlexpression values. Unsupervised hierarchical clustering then was appliedto the dataset. Hierarchical clustering was performed using Cluster andvisualized using TreeView (M. Eisen, Stanford; available on the internetat rana.lbl.gov). This analysis identified gene expression patterns thatdifferentiated most SLE patients from healthy controls. Thirty-seven ofthe 48 SLE patients clustered tightly together, while 11 of the patientsco-clustered with controls. Six of the 42 control subjects clusteredtogether with the large group of patients.

Most (124 of 161, 77%) of the genes that best distinguished SLE fromcontrol PBMCs were expressed at higher levels in SLE patients than innormal subjects. These are presented in Table 2. A number of these geneshave known or suspected roles in the immune system. For example, manySLE patients were found to overexpress mRNA for the following cellsurface markers: TNFR6 (Fas/CD95), a death receptor; ICAM-1 (CD54), anadhesion molecule; CD69, an activation antigen; and complementreceptor 1. Of interest, three different Fc receptors were expressed atelevated levels: the Fc receptor for IgA (FCAR, CD89), and the IgGreceptors FcRγIIA (CD32) and FcRγI (CD64). Three molecules in theinflammatory IL-1 cytokine pathway—IL-1β, the IL-1 receptor II (IL-1RII), and the IL-1 receptor antagonist—also were generallyoverexpressed. Interestingly, Jagged 1, a ligand for Notch 1 located inthe SLE susceptibility interval on chromosome 20p, also wasoverexpressed in some patients. Other notable genes that wereoverexpressed in SLE patients include the signaling molecules MAP3K-8,RAB27, interleukin-6 signal transducer, the transcription factors v-ets2, MADS box transcription factor 2, and the estrogen responsive zincfinger protein 147.

A number of genes were expressed at lower levels in patients thancontrols. These are presented in Table 3, and included T cell genes suchas Lck, TCR delta, and TCR beta. Flow cytometry of freshly stained PBMCswas used to confirm that there was a T cell lymphopenia in many of thepatients (i.e., about a 20% decrease, on average, in percentage of CD3⁺T cells). The patients also demonstrated a significant increase in thepercentage of monocytes, as compared to the percentage of monocytes incontrols. Specifically, PBMC populations from SLE patients (n=18)contained 52% T cells, 5% B cells, 28% monocytes/macrophages, and 15% NKcells. PMBC populations from control subjects (n=28) contained 65% Tcells, 6% B cells, 13% monocytes/macrophages, and 16% NK cells. Thepercentages of T cells (p=0.014) and monocytes (p=0.00001) thus differedbetween SLE and controls. These differences in baseline cell populationsclearly contribute to some of the differences in gene expressionobserved, and highlight the importance of documenting cell percentagesin mixed cell populations.

Identification of IFN-regulated genes: One of the most striking mRNAclusters contained several genes previously identified as beinginterferon-regulated (Der et al. (1998) Proc. Natl. Acad. Sci. U.S.A.95:15623). Interferons are highly active cytokines important formaintaining viral immunity (IFN-α and IFN-β) and for mediating TH1immune responses (IFN-γ). Genes in this cluster were up-regulated inabout half of the SLE patients, and were expressed at low levels in mostof the control subjects.

Experiments were conducted to examine the extent to which the genes inthis cluster could be regulated in PBMCs by IFN treatment in vitro.Peripheral blood was drawn from each of four healthy controlindividuals. PBMCs were isolated over Lymphocyte Separation Medium(Mediatech Cellgro, Herndon, Va.) according to the manufacturer'sprotocol. After the last wash, cells were resuspended in complete media(RPMI1640, 10% heat inactivated FBS, 2 mM L-glutamine, pen/strep) at afinal concentration of 2×10⁶ cells/mL. PBMCs were cultured for six hoursat 37° C. with the following additions:

(i) PBS+0.1% BSA control,

(ii) IFN-α and IFN-β (R&D Systems, Minneapolis, Minn.), each at 1000U/mL in PBS+0.1% BSA, and

(iii) IFN-γ (R&D Systems, Minneapolis, Minn.), 1000 U/mL in PBS+0.1%BSA.

Following the incubation, total RNA was isolated, and cRNA probes wereprepared for chip hybridization. Data were analyzed using MAS 4.0software, and all chips were scaled to 1500. Absent and low expressionvalues were assigned a threshold of 20 AD units as described above.Genes that met both of the criteria below in all four experiments wereidentified as IFN-regulated:

(i) change in expression of at least 2-fold when compared to untreatedcontrol, and

(ii) difference in expression of at least 500 AD units when compared tountreated control.

Changes in gene expression following IFN treatment were assessedrelative to a six-hour control culture. This analysis identified 286genes that demonstrated more than a 2-fold change in expression frombaseline, and an absolute mean difference in the level of expression ofgreater than 500 units. The induction of many known IFN-regulated genes,such as Stat1, myxovirus resistance 1 (Mx-1), and ISGF-3, validated theapproach. Using this list of IFN-regulated genes, 13 of 14 unique genesin the cluster were identified as bona fide IFN-regulated transcripts.Overall, 23 of the 161 genes (14.3%) were found to be IFN-regulated,compared with 7 genes (4.3%) that would have been expected by chancealone. The overrepresentation of interferon-regulated genes in the listof transcripts that best discriminated SLE patients from controls wasconsistently observed when a variety of different filters were used todefine both IFN-regulated and SLE genes.

The mRNA levels of the IFNs themselves were not significantly differentbetween patients and controls. Plasma/serum IFN-γ and IFN-α proteinswere measured by ELISA (Pierce Endogen, Rockford, Ill.). IFN-γ wasundetectable in all samples (less than 25 pg/mL). IFN-α was detectablein only two patients (26 and 29 pg/mL) and one control subject (56pg/mL).

An IFN “score” was calculated for each patient and control, based onexpression of genes in the IFN cluster. Scores were calculated by firstnormalizing the expression values within each row of genes so that themaximum value in any row was 1.0. Then the columns (samples) were summedto obtain the score. The IFN score (mean±SD) for patients was 3.7±2.6,compared to controls 1.5±0.5, p=4.2×10⁻⁷. Approximately half of the SLEpatients exhibited an elevated IFN score, while the others had scoresindistinguishable from controls (FIG. 1).

The lupus patient population was divided into two groups, with theIFN-high group containing the 24 patients with the highest IFN scores,and the IFN-low group containing the 24 patients with the lowest scores.Differences in gene expression were examined. Table 4 contains a list ofthe genes that displayed differential expression between the IFN-highand IFN-low groups. All of the genes listed in Table 4 were expressed ata greater level in the IFN-high group that in the IFN-low group.

Studies then were conducted to determine whether the IFN gene expressionsignature correlated with clinical features of SLE. SLE typically isdiagnosed using eleven criteria developed by the ACR (Hochberg (1997)Arthritis Rheum. 40:1725). These criteria span the clinical spectrum ofSLE and include skin criteria (malar rash, oral ulcers,photosensitivity, and discoid rash), systemic criteria (pleuritis orpericarditis, arthritis, renal disease, or CNS involvement), andlaboratory criteria (cytopenias, anti-dsDNA or anti-phospholipid Abs,and antinuclear antibodies). A patient must meet four of these criteriato be classified as having definite SLE. The number of SLE criteria metby each patient was plotted against his or her IFN score (FIG. 2). Thisanalysis revealed that the IFN score was correlated with the number ofSLE criteria displayed in each patient.

In a similar analysis, the clinical features of the 24 SLE patients withthe highest IFN scores (IFN-high) were compared to the clinical featuresof the 24 SLE patients with the lowest scores (IFN-low). As depicted inFIG. 3, patients in the IFN-high group had a significantly higher numberof SLE criteria (6.8±1.3) than those in the IFN-low group (5.7±1.1;p=0.004). Patients in the IFN-high group also showed a trend towardsbeing diagnosed with SLE at an earlier age (25±12 compared with 30±13years; p=0.192). Importantly, 15 of 24 patients (63%) in the IFN-highgroup fulfilled the ACR criteria for involvement of kidneys and/or theCNS, the most serious complications of lupus, compared with 5 of 24patients (21%) in the IFN-low group (FIG. 4). In addition, 18 of 24IFN-high patients (75%) showed hematologic involvement in their disease(severe leukopenia, hemolytic anemia or thrombocytopenia), compared withonly 5 of 24 IFN-low patients (21%). An elevated interferon score thuscorrelated with the more severe manifestations of SLE.

The hypothesis that IFNs are important in the pathogenesis of lupus issupported by a number of observations. Mice transgenic for IFN-γ developlupus-like autoimmunity (Seery et al. (1997) J. Exp. Med. 186:1451), andlupus-prone NZB/NZW F1 mice treated with anti-IFN-γ Abs or bred onto theIFN-γ^(−/−) background show amelioration of disease (Jacob et al. (1987)J. Exp. Med. 166:798; and Balomenos et al. (1998) J. Clin. Invest.101:364). The interferon-inducible gene IFI-202 has been identified asan SLE gene within the Nba2 SLE locus on mouse chromosome 1, NZB mice,the parental strain for this locus, show constitutively high expressionof this transcription factor (Rozzo et al. (2001) Immunity 15:435). Inhumans, elevated levels of IFN-α have been reported in the sera of someSLE patients (for review see Ronnblom and Alm (2001) J. Exp. Med.194:59), and a significant percentage of individuals treated with IFN-αfor viral hepatitis develop lupus-related autoantibodies (Fukuyama etal. (2000) Am. J. Gastroenterol. 95:310). Finally, IFN-α in the sera ofsome pediatric SLE patients induces maturation of monocytes into highlyactive antigen-presenting plasmacytoid dendritic cells (Blanco et al.(2001) Science 294:1540).

While genes in IFN-signaling pathways exhibited dysregulated expressionin some lupus patients, the mRNA levels of the IFNs themselves were notsignificantly different between patients and controls. IFN-γ protein wasnot detectable by ELISA in any patient or control sample, and IFN-α wasdetectable in only 2 of 48 patients and 1 of 42 controls. Thus, othercytokines that utilize Jak/Stat signaling pathways downstream of theirreceptors, such as IL-4, IL-13, or IL-2 (Hirano et al. (2000) Oncogene19:2548), could contribute to the gene expression patterns observed.

Example 2 Identifying Additional Genes that can be Used to Diagnose SLE

Study participants: Patients were enrolled from the lupus clinic atJohns Hopkins University Medical Center (Petri et al. (1991) ArthritisRheum. 34:937-944). All SLE patients had physician-verified SLE and wereevaluated by the same examining physician. After informed consent,patients provided a peripheral blood sample. Blood for RNA extractionwas collected into PaxGene tubes (PreAnalytiX, Hombrechtikon,Switzerland).

Sample Processing and Chip Hybridization: RNA was extracted using thePaxGene Blood RNA System (PreAnalytix). Five μg of total RNA was used toprepare biotinylated cRNA for hybridization using the standardAffymetrix protocol (Expression Analysis Technical Manual, Affymetrix,Santa Clara, Calif.). For seven samples with low RNA yields, two roundsof amplification were performed. Fifteen micrograms of each labeled cRNAwas hybridized to Affymetrix U133A Human GeneChips.

Data Processing Affymetrix Microarray Suite (MAS) 5.0 software was usedto generate expression (“signal”) values for each gene. To correct forslight differences in overall chip hybridization intensity and allow forcomparison between samples, each chip was scaled to an overall intensityof 1500.

Comparison Analyses and Hierarchical Clustering: For selection of genesthat were differentially expressed between the 81 SLE patients and 41controls, the following three criteria were used: (i) p<1×10⁻⁵ byunpaired Student's t test, (ii) change in expression of at least1.5-fold when comparing the means of the two groups, and (iii)difference in expression of at least 100 signal units when comparing themeans of the two groups. A set of 405 genes met all three of thesecriteria and were selected for further analysis.

Hierarchical clustering was performed with CLUSTER and visualized withTREEVIEW (Eisen et al. (1998) Proc. Natl. Acad. Sci. USA95:14863-14868). Prior to clustering, each data point for a given genewas divided by the mean expression value of the controls for that gene.The log₂ of these ratios was then used as input for CLUSTER. Data weretransformed in the same manner for k-means clustering using the samesoftware package.

Calculation of Gene Expression Signature Scores: For calculation ofsignature scores, the expression values within each gene row werenormalized so that the maximum value in any row was 1.0. For eachsample, the normalized values for each gene in the signature were thensummed to obtain the score. These scores were used to correlate geneexpression signatures with clinical features. P-values for thesecorrelations were generated by linear regression analysis. Signaturescores also were used to calculate correlation coefficients between thevarious signatures in order to assess their interdependence.

Cell Sorting for Expression Profiling of Purified Cell Subsets: Forisolation of T cells, NK cells, monocytes, and neutrophils, blood wascollected from healthy donors into ACD tubes (Becton-Dickinson, FranklinLakes, N.J.). Total WBCs were separated from RBCs using Lympholyte-Poly(Cedarlane Labs, Hornby, Ontario) according to the manufacturer'sprotocol. Any remaining RBCs were removed with RBC lysis buffer (RocheApplied Science, Basel, Switzerland). After blocking with 10% humanserum, cells were stained for 15 minutes at 4° C. with CD3-APC,CD66B-FITC, CD64-CyC, and CD56-PE, and then washed with cold PBS+2%fetal bovine serum. A four-color, four-way sort was performed with theFacsVantage SE Turbo with FACS Diva option (BD Biosciences, San Jose,Calif.). Purity of populations was >90%. B cells were isolated followingleukopheresis of control donors using a Miltenyi system for positiveselection of CD19+ cells. RNA was isolated from the purified cell typesusing the RNeasy kit (Qiagen, Valencia, Calif.) and prepared forhybridization as described above.

Functional classes represented among differentially expressed genes:Blood samples were collected from 81 patients and from 41 healthycontrols. Total RNA was isolated from WBCs and used to generate cRNAprobes for hybridization to Affymetrix U133A GeneChips. The expressionlevels of 22,283 probe sets (representing 18,400 transcripts andvariants) were compared between SLE patients and normal controls. 470probe sets were identified (representing 405 transcripts) that met allthree of the following criteria for differential expression: (i)p<1×10⁻⁵ by unpaired students t test, (ii) at least a 1.5-fold changebetween the SLE mean and control mean, and (iii) a difference of atleast 100 signal units between the SLE mean and control mean.

To visualize the differences in gene expression between patients andcontrols, the data were transformed for each gene by dividing eachsignal value by the mean signal of the controls. Hierarchical clusteringwas then performed using the log₂ of this ratio. The majority ofpatients were clustered together in this analysis, with the exception oftwo patients that clustered with the controls. There also were fourcontrols that clustered in the SLE group. The enrichment ofIFN-regulated genes, as identified by a previously described in vitrostimulation of normal PBMCs with IFN (Baechler et al. (2003) Proc. Natl.Acad. Sci. USA 100:2610-2615), was immediately apparent (90 genes). Inparticular, there was a tight cluster of 82 genes, 69 of which wereinduced by IFN in the in vitro experiment. This set of 82 genes wasidentified as the IFN signature, and this expression pattern wasobserved in ˜75% of the patients. The majority of the IFN-regulatedgenes in this cluster were up-regulated by type I IFN (67 of 69 genes,average fold change greater than 2 in four in vitro experiments); and,many also were induced by type II IFN (IFN-γ; 48 of 69 genes with foldchange greater than 2). The level of induction of these genes, measuredby fold change relative to PBS control, generally was greater inresponse to IFN-α/β as compared to IFN-γ. Seventy of the genescomprising the IFN signature are listed in Table 5. In addition to thegenes listed in Table 5, the IFN signature included the following: XIAPassociated factor-1, LY6E, phospholipid scramblase 1, capicua homolog,2′-5′-OAS-like, hypothetical (osteoblast), IFN-stimulated ptn 15 kDa, C1inhibitor, IFN-alpha inducible (IFI-6-16), CD64, galectin 3 (lectin,galactosidase-binding, soluble 3 binding protein), and MX1 (myxovirusresistance 1). Twenty-one other IFN-regulated genes were not included inthe IFN signature because their expression was not correlated with SLEactivity. These are listed in Table 6.

TABLE 5 IFN signature Accession Number Gene AA740186 biliverdinreductase A NM_003113 SP100 NM_006442 DR-associated ptn 1 U03891APOBEC3A (phorbolin 1) NM_004335 BST-2 NM_030776 Z-DNA binding protein 1D43949 hypothetical KIAA0082 NM_005502 ATP-binding cassette A1 AW474434AW474434 NM_018295 FLJ11000 NM_015675 GADD45B NM_001712 biliaryglycoprotein NM_002450 metallothionein 1L M10943 metallothionein 1FNM_000593 ATP-binding cassette B AW188198 TNF-alpha induced protein 6BC002666 guanylate binding protein 1 AF317129 torsin B NM_004223 UBE2L6NM_016381 3′ repair exonuclease 1 NM_003641 IFIT-1 (9-27) BF338947IFIT-3 AL121994 hypothetical AL121994 NM_005953 metallothionein 2ANM_005952 metallothionein 1X NM_023068 sialoadhesin NM_017414 ubiquitinspecific protease 18 NM_017631 hypothetical FLJ20035 NM_005532 IFN-alphainducible 27 NM_006187 2′-5′-OAS 3 AK002064 DNApolymerase-transactivated protein 6 (DKFZP564A2416 protein) AA083478tripartite motif-containing 22 NM_016816 2′,5′-OAS 1 NM_004030 IFNregulatory factor 7 NM_001549 IFIT-4 BE049439 IFN-induced protein 44NM_001548 IFIT-1 NM_016323 cyclin-E binding protein 1 NM_022750 poly(ADP-ribose) polymerase family, member 12 (hypothetical FLJ22693)NM_016817 2′-5′-OAS 2 NM_022147 28 kD IFN responsive protein N47725retinoic acid and IFN-inducible NM_015907 leucine aminopeptidaseBC001356 IFN-induced protein 35 NM_017912 hect domain and RLD 6(hypothetical protein FLJ20637) NM_002463 MX2 NM_005138 SCO2 U65590 IL-1receptor antagonist AI719655 caspase 1 U57059 TNF SF10 NM_004688 N-myc(and STAT) interactor NM_006519 t-complex-associated 1-like 1 NM_002970N1-acetyltransferase NM_005531 IFN-gamma inducible 16 BF055474 NY-REN-34antigen NM_002201 IFN stimulated gene (20 kD) NM_007315 STAT1 NM_022168IFI-H1 NM_014314 RNA helicase AI421071 CCR1 AL031602 IBR domaincontaining 3 (hypothetical AL031602) BF217861 metallothionein 1ENM_005951 metallothionein 1H NM_017654 sterile alpha motif domaincontaining 9 (hypothetical FLJ20073) NM_002675 promyelocytic leukemiaNM_014398 LAMP3 NM_014628 MAD2L1 binding protein (hypothetical NM_14628)NM_005771 retinol dehydrogenase homolog NM_024021 membrane-spanning4-domains, subfamily A, member 4 (CD20) AI337069 radical S-adenosylmethionine domain containing 2 (AI337069)

TABLE 6 IFN-regulated genes not included in the IFN signature AccessionNo. Gene BC005907 histamine N-methyltransferase (BC005907) NM_015961Chromatin modifying protein 5 (NM_015961) NM_001803 CDW52 antigen(CAMPATH-1 antigen) BF590263 chondroitin sulfate proteoglycan 2(versican) NM_005213 cystatin A (stefin A) U08092 histamineN-methyltransferase NM_000416 interferon gamma receptor 1 BG540628immunoglobulin kappa constant NM_001565 small inducible cytokinesubfamily B (Cys-X-Cys), member 10 NM_002759 protein kinase,interferon-inducible double stranded RNA dependent NM_002818 proteasome(prosome, macropain) activator subunit 2 (PA28 beta) NM_021136 reticulon1 NM_005621 S100 calcium binding protein A12 (calgranulin C) AI056051JAK binding protein BE962483 tripartite motif-containing 14 NM_014857RAB GTPase activating protein 1-like (KIAA0471 gene product) NM_006406peroxiredoxin 4 AV699744 KIAA0650 protein AI082078 translocase of innermitochondrial membrane 10 homolog (yeast) NM_016184 C-type (calciumdependent, carbohydrate-recognition domain) lectin, superfamily member 6NM_016619 placenta-specific 8 (hypothetical protein)

In addition to the IFN signature, several other functionally interestinggene groups were identified among the transcripts differentiallyexpressed in SLE. There were 29 genes encoding ribosomal proteinsubunits among the 405 differentially expressed genes. One particularlytight cluster was specifically enriched for ribosomal transcripts (14 of15 transcripts). There also were 35 transcripts encoding mitochondrialproteins that were over-expressed in the lupus samples. Interestingly,the expression patterns of the ribosomal genes and the mitochondrialgenes were highly similar across the lupus patients. In order to assessthe degree of similarity between these two signatures, a ribosomal scorewas calculated using the 15-gene cluster, and a mitochondrial score wascalculated using the 35 mitochondrial genes. These scores were veryhighly correlated (r=0.87), indicating that the two signatures can beconsidered as one (Table 7).

TABLE 7 Ribosomal/mitochondrial signature Ribosomal MitochondrialAccession Accession No. Gene No. Gene L05095 RPL30 NM_014180 mito.ribosomal protein L22 BE968801 RPL35A NM_016055 mito. ribosomal proteinL48 NM_001032 RPS29 NM_014018 mito. ribosomal protein S28 N32864 HINT1BE782148 mito. ribosomal protein L42 AA320764 RPS10 BC003375 mito.ribosomal protein L3 NM_000988 RPL27 NM_006636 MTHFD2 NM_001019 RPS15aNM_004889 ATP5J2 BC001019 RPL39 NM_004373 COX6A1 NM_000971 RPL7NM_001866 COX7B NM_001006 RPS3A NM_006830 UQCR AI348010 RPL31 NM_006886ATP5E NM_000661 RPL9 NM_001685 ATP5J NM_001021 RPS17 NM_014402 QP-CAI805587 RPS7 NM_020548 diazepam binding inhibitor NM_004374 COX6CNM_001867 COX7C NM_004894 chr 14 ORF 2 NM_006476 ATP5L NM_001865 COX7A2NM_005174 ATP5C1 NM_004546 NADH dehyd. (ubiquinone) 1 beta 2 NM_002489NADH dehyd. (ubiquinone) 1 alpha 4 NM_006004 UQCRH NM_001697 ATP5ONM_016071 mito. ribosomal protein S33 BC002772 NADH dehyd. (ubiquinone)1 alpha 6 NM_002491 NADH dehyd. (ubiquinone) 1 beta 3 AF313911thioredoxin NM_006406 peroxiredoxin 4 NM_004545 NADH dehyd. (ubiquinone)1 beta 1 NM_016622 mito. ribosomal protein L35 NM_020139 oxidoreductaseUCPA NM_012459 TIMM8B NM_006327 TIMM23

Three additional genes encoding mitochondrial proteins were expressed atlower levels in SLE. Also among the genes down-regulated in SLE was atight cluster of genes that exhibited a more dramatic decrease inexpression in a subset of samples (30 transcripts). Many of these genesencode proteins related to transcription or other nuclear processes,including the transcriptional regulators retinoblastoma-like 2 (RBL2),F-box and leucine-rich repeat protein 11 (FBXL11), and nuclear receptorsubfamily 1, group D, member 2 (NR1D2), as well as other nucleic acidbinding proteins such as chromodomain helicase DNA binding protein 4(CHD4), KH domain containing, RNA binding, signal transductionassociated 1 (KHDRBS1), serine/arginine repetitive matrix 2 (SRRM2), andRAD21. These are listed in Table 8.

TABLE 8 Nuclear/transcription signature Accession No. Gene N32859 NR1D2NM_004486 golgi autoantigen A2 AI761771 CHD4 BG289967 RAD21 homologX76061 retinoblastoma-like 2 NM_014857 RAB GTPase activating protein1-like (KIAA0471) NM_006559 KHDRBS1 BE538424 BE538424 Y09216 DYRK2AK001699 F-box only protein 21 NM_003316 TTC3 NM_002185 IL7R AI557319AI557319 AW149364 SFRS protein kinase 2 NM_004719 SFRS2IP NM_016333SRRM2 NM_012201 Golgi apparatus protein 1 NM_000565 IL6R NM_002385myelin basic protein NM_005892 formin-like U48734 actinin, alpha 4AW237172 Jumonji domain containing 2B (KIAA0876 protein) NM_007371bromodomain-containing 3 AI356398 zinc finger protein 36 AK022014 Akinase (PRKA) anchor protein 13 (hypothetical protein FLJ11952) AK024505f-box and leucine-rich repeat 11 AI830698 IGF1R AI741124 G protein, beta1 BF246499 Tyrosine 3-monooxygenase/tryptophan 5-monooxygenaseactivation protein, beta polypeptide (GW128 protein) NM_018340hypothetical protein FLJ11151

Another notable group of genes included a set of 28 transcripts whoseexpression was correlated with the percentage and absolute number ofneutrophils in the patients' blood samples. In order to determine ifthese genes were specifically expressed in neutrophils, microarrayanalysis was performed on purified populations of T cells, B cells, NKcells, monocytes, and neutrophils from normal donors. Of these 28 genes,13 were highly expressed in neutrophils as compared to other WBC subsets(fold change of at least 10 when compared to at least one other celltype; Table 9). Several of these genes were also highly expressed inmonocytes, but the expression of these genes in the patient populationdid not correlate with the percentage or number of monocytes in thepatients' blood samples.

TABLE 9 Neutrophil signature Accession No. Gene NM_004666 vanin 1NM_003853 IL18R accessory protein AF153820 KCNJ2 NM_004334 BST1 AL353759histone 1, H2ac (H1 histone family, member 4) NM_004049 BCL2-relatedprotein A1 M63310 annexin A3 AB014550 KIAA0650 protein NM_004125 Gprotein, gamma 10 NM_015364 lymphocyte antigen 96 (MD-2 protein)NM_002964 S100 calcium binding ptn A8 NM_005621 S100 calcium binding ptnA12 NM_005213 cystatin A

Patterns of heterogeneity in lupus blood: The expression patterns of thegene groups suggest that, in addition to contributing to the distinctionbetween SLE patients and normal controls, these signatures reflect asignificant degree of heterogeneity within the patient population. As anunsupervised method of identifying patient subgroups, the patientsamples were subjected to k-means clustering. As input for theclustering, log₂-transformed expression ratios (sample signal divided bycontrol mean signal) were used for 151 genes (82 IFN signature genes, 15ribosomal signature genes, 11 mitochondrial signature genes, 13neutrophil signature genes, and 30 nuclear/transcription signaturegenes). Following k-means analysis (k=4), one-dimensional hierarchicalclustering of the same 151 genes was performed with the sample orderfixed according to the subgroups defined by k-means clustering. Thek-means algorithm identified the following four subsets of SLE patients:(i) nuclear/transcription positive, ribosomal/mitochondrial positive,IFN positive (n=11); (ii) mitochondrial/ribosomal positive, IFN negative(n=21); (iii) mitochondrial/ribosomal negative, IFN positive (n=25);(iv) ribosomal/mitochondrial positive, IFN positive (n=24).

The IFN signature correlates with disease severity and immunologicabnormalities: In order to assess the potential association of thesegene expression signatures with clinical manifestations of SLE,correlation coefficients were calculated between the signature scoresand clinical features. The significance of the correlations wasdetermined by linear regression analysis. In order to visualize thecorrelations in the context of the clustering result, correlationcoefficients also were calculated between the expression of eachindividual gene and the clinical features, and correlation curves wereplotted as moving windows (11-gene average).

The IFN signature was highly correlated with disease activity asmeasured by the SLE disease activity index (SLEDAI; Table 10). Thecorrelation between the IFN score and SLEDAI was highly significant(r=0.38, p=3.9×10⁻⁴ by linear regression). Several laboratory measuresoften associated with disease activity, such as leukopenia and elevatederythrocyte sedimentation rate (ESR), also were correlated with the IFNsignature (ESR, r=0.38, p=5.4×10⁻⁴; WBC, r=−0.38, p=4.7×10⁻⁴). Bodyweight was significantly decreased in IFN-high patients (r=−0.46,p=1.7×10⁻⁵). Patients with high IFN scores were more likely to haverequired cytotoxic therapy at some point in their disease course(r=0.27, p=0.02), although they were not more likely to be receivingimmunosuppressive therapy at the time of blood draw (r=0.07, p=0.56).Perhaps as the ultimate measure of historical disease activity, a numberof patients in the study have required hospitalization at some pointbecause of their lupus (n=40). The number of hospitalizations perpatient ranged from 1 to 10 (mean=2.9, SD=2.5). The number of SLEhospitalizations was positively correlated with the IFN score (r=0.31,p=0.009). A smaller number of patients required hospitalization forinfectious complications (n=17); this was slightly, thoughnon-significantly, correlated with IFN score (r=0.23, p=0.06). Thesedata support the conclusion that the IFN signature is a marker forsevere and active SLE.

Another striking result of this analysis is the strong evidence linkingthe IFN signature with immunologic abnormalities, both current andhistorical. The IFN signature exhibited strong negative correlation withcurrent visit C3 and C4 levels (C3, r=−0.47, p=1.0×10⁻⁵; C4, r=−0.37,p=6.4×10⁻⁴). Accordingly, IFN scores were higher in the subset ofpatients fulfilling the SLEDAI component for low complement (n=30, IFNscore 32.8±10.7) than in patients who did not fulfill this component(n=51, IFN score 23.5±11.4, p=4×10⁻⁴). The IFN score was also correlatedwith a history of low complements (low C3, r=0.51, p=4.0×10⁻⁶; low C4,r=0.34, p=0.003).

The autoantibody profiles of these SLE patients also correlated with theexpression of the IFN signature genes. Both current visit and historicalpresence of antibodies against dsDNA correlated positively with the IFNscore (current anti-DNA titer, r=0.37, p=6.2×10⁻⁴; historical anti-DNA,r=0.53, p=1.1×10⁻⁶). The historical presence of antibodies against RNAbinding proteins exhibited a trend towards correlation with IFN scores,although not statistically significant in all cases (anti-Ro, r=0.30,p=0.01; anti-La, r=0.23, p=0.05; anti-RNP, r=0.21, p=0.07). Despite thenon-significant p-value of the anti-RNP correlation, the IFN scores ofpatients who at some point tested positive for anti-RNP (n=23, IFN score30.4±7.1) were significantly higher than those of patients who havenever tested positive for anti-RNP (n=49, IFN score 25.4±12.2, p=0.03).Finally, the presence of anti-erythrocyte antibodies was correlated withIFN score (Coombs test, r=0.29, p=0.04). This finding is consistent withthe observation that patients with elevated IFN scores were more likelyto have experienced anemia during their disease course (r=0.26, p=0.03).Taken together, these data show that immunological abnormalities are aprominent feature of lupus patients that exhibit the IFN signature.

TABLE 10 Correlation of IFN signature with clinical features Clinicalr-value with Correlation Feature IFN score p-value Clin+ vs. Clin− curveCurrent SLEDAI 0.38 3.9E−04 0.002 p < 0.005 Low C′ 0.38 5.1E−04 4.2E−04p < 0.001 Inc. anti-DNA 0.38 4.8E−04 4.4E−04 p < 0.0005 ESR 0.38 5.4E−04p < 0.001 RDW 0.29 0.023 p < 0.05 C3 −0.47 1.0E−05 p < 0.0001 C4 −0.376.4E−04 p < 0.005 anti-DNA titer 0.37 6.2E−04 6.7E−04 p < 0.005 HCT−0.32 0.002 p < 0.0005 HGB −0.33 0.007 p < 0.005 WBC −0.38 4.7E−04 p <0.005 Lymph # −0.42 8.8E−04 p < 0.005 Lymph % −0.29 0.024 p < 0.01Neutro % 0.27 0.032 p < 0.01 Weight −0.46 1.7E−05 p < 0.0005 BPdiastolic −0.24 0.034 p < 0.05 BP systolic −0.28 0.013 p < 0.05Historical #SLE hosp. 0.31 0.009 0.002 p < 0.05 # Infect. hosp. 0.230.055 0.046 p < 0.05 Low C3 0.51 4.0E−06 1.6E−06 p < 0.0001 Low C4 0.340.003 0.002 p < 0.005 Anti-DNA 0.53 1.1E−06 3.6E−08 p < 0.0005 Anti-Ro0.30 0.011 0.012 p < 0.05 Anti-La 0.23 0.049 0.062 ns Anti-RNP 0.210.073 0.032 ns Coombs 0.298 0.044 0.019 p < 0.05 Anemia 0.26 0.028 0.027p < 0.05 Cytotoxic 0.27 0.022 0.017 p < 0.05

TABLE 11 Correlation of ribosomal/mitochondrial signature with clinicalfeatures Clinical r-value with Correlation Feature IFN score p-valueClin+ vs. Clin− curve Current Inc. anti-DNA −0.29 0.009 0.007 p < 0.005Neutro % −0.27 0.033 p < 0.05 Historical Photosensitivity −0.25 0.0290.027 p < 0.05 NSAIDs 0.27 0.021 0.022 p < 0.05

TABLE 12 Correlation of neutrophil signature with clinical featuresr-value with Clin+ vs. Correlation Clinical Feature IFN score p-valueClin− curve Current Pred. dose 0.133 0.003 p < 0.05 WBC 0.29 0.010 p <0.05 Neutro % 0.54 7.9E−06 p < 0.0005 Neutro # 0.37 0.003 p < 0.05 Lymph% −0.55 5.2E−06 p < 0.0005 Mono % −0.40 1.4E−03 p < 0.005 HistoricalRaynaud's 0.35 0.003 0.002 p < 0.01 Lupus anticoag 0.61 0.007 0.040 p <0.05 Thrombocytopenia 0.32 0.005 0.020 p < 0.05 NSAIDs 0.24 0.040 0.025p < 0.05

TABLE 13 Correlation of nuclear/transcription signature with clinicalfeatures Clinical r-value with Correlation Feature IFN score p-valueClin+ vs. Clin− curve Current Neutro # 0.28 0.031 p < 0.005 HistoricalAnti-RNP −0.24 0.046 0.054 ns Anti-DNA −0.24 0.038 0.042 p < 0.05Anti-SM −0.24 0.038 0.050 ns Anemia −0.28 0.016 0.016 p < 0.05Proteinuria −0.36 0.002 0.002 p < 0.01 Hematuria −0.23 0.054 0.085 ns

Tables 10-13 list clinical features significantly correlated with geneexpression signatures. Clinical manifestations present either at thetime of blood draw (Current) or at some point in the patient's history(Historical) were correlated with the indicated gene expressionsignatures. Correlations are presented as r-values, with p-valuesderived from linear regression. For clinical features where the patientis either positive or negative for the feature (e.g., Anti-DNAantibodies), signature scores of patients positive for the feature(Clin+) were compared to the scores of patients negative for the feature(Clin−). The p-values from unpaired t-test of these two groups arepresented in the “Clin+ vs. Clin−” columns. For SLEDAI, the comparisonwas between patients with SLEDAI ≦1 and patients with SLEDAI ≧6. Randompermutation analysis was used to generate p-values. ns, not significant(p>0.05); C′, complement; Inc., increased.

Ribosomal/mitochondrial signature: Fifteen genes encoding ribosomalprotein subunits, together with HINT1, a histidine triad nucleotidebinding protein whose physiological function is unknown, formed a tightcluster in the hierarchical clustering of all 405 SLE genes. A group of35 genes encoding mitochondrial proteins displayed an expression patternremarkably similar to the ribosomal pattern. Indeed, the ribosomalsignature score and the mitochondrial score were highly correlated(r=0.87). Since the mitochondrial genes included a number of cytochromeC oxidase subunits (5 of 35 mitochondrial genes) as well as severalsubunits of the F₁F₀ ATP synthase (6 of 35 genes), experiments wereconducted to examine the ability of 11 classical mitochondrial genes tosubstitute for the entire set of 35 mitochondrial genes observed in theSLE dataset. The signature consisting of the 11 classical genes wasnearly identical to the full mitochondrial signature (r=0.98) andremained highly correlated with the ribosomal signature (r=0.89). Giventhis striking degree of similarity, the 11 core mitochondrial genes andthe 15 ribosomal cluster genes were considered as a single geneexpression signature (the ribosomal/mitochondrial signature).

Although there were few clinical features significantly correlated withthe ribosomal/mitochondrial signature, one notable finding was thenegative association of this signature with antibodies against DNA(Table 11). The ribosomal/mitochondrial score was inversely correlatedwith fulfillment of the SLEDAI component for anti-DNA antibodies(r=−0.29, p=0.009). Although the score was not significantly correlatedwith the anti-DNA titer at the time of blood draw, patients that lackedanti-DNA antibodies (n=48, IFN score 9.3±4.4) had higherribosomal/mitochondrial scores than patients that tested positive foranti-DNA (n=33, IFN score 7.1±4.0, p=0.02). This signature alsoexhibited a negative correlation with photosensitivity (r=−0.25, p=0.03)and with the percentage of neutrophils in the patients' blood samples(r=−0.27, p=0.03).

Neutrophil signature: Using the expression of the 13 neutrophil genes tocalculate the neutrophil score, it was observed that expression of thesegenes correlated positively with the current dose of prednisone (Table12; r=0.33, p=0.003). Prednisone leads to the de-margination ofneutrophils from vascular endothelium, which may account for thisassociation. Interestingly, the neutrophil signature was alsosignificantly correlated with a history of Raynaud's phenomenon (r=0.35,p=0.003). Also, although not correlated with current visit plateletcounts, the signature was correlated with a history of low platelets(r=0.32, p=0.005). Despite the small number of patients for which datawas available for the presence of lupus anticoagulant (LAC; 18 patientshad data available, 4 were positive for LAC), the correlation betweenthe neutrophil score and LAC was high enough to achieve statisticalsignificance (r=0.61, p=0.007). This result must be interpreted withcaution due to the small sample size.

Nuclear/transcription signature: The primary distinguishing feature ofthe genes that were decreased in expression in SLE was a group of 30genes that exhibited a more dramatic change in a subset of patients.Many of these genes are known to have functions related to transcriptionor other nuclear processes. Because the expression of these genes isdecreased in SLE, the patients with a greater fold-decrease inexpression are said to carry the nuclear/transcription signature.

Interestingly, the expression of these genes was negatively correlatedwith several lupus autoantibodies (Table 13). In particular, antibodiesagainst some ribonucleoprotein components were found more frequently inthe patients carrying the nuclear/transcription signature (i.e., thosewith lower expression of those genes). This was true for anti-Sm andanti-RNP (Sm r=−0.24, p=0.04; RNP r=−0.24, p=0.05) but not for anti-Roor anti-La (Ro r=−0.01, p=0.92; La r=0.08, p=0.53). A positive anti-DNAtest at some point during disease course was also inversely correlatedwith the nuclear/transcription score (r=−0.24, p=0.04). This expressionsignature also correlated negatively with a history of anemia (r=−0.28,p=0.02). Also considering the patient's history, a negative correlationwas observed with two measures of kidney involvement (proteinuriar=−0.36, p=0.002; hematuria r=−0.23, p=0.05).

Patient subsets defined by presence or absence of multiple genesignatures: Although the signature score approach reveals interestingclinical correlations, it does not account for clinical features thatmight be dependent upon the combined presence or absence of more thanone signature. In order to identify such features, the clinical profilesof the four lupus subsets identified were compared by k-means clusteringof 151 SLE genes. Visualization of the clustering result revealed thatthese patient subsets are defined by the presence or absence of threesignatures: IFN, ribosomal/mitochondrial, and nuclear/transcription. Thesignature combinations defining the four patient groups are as follows:Group 0, ribosomal/mitochondrial positive, IFN positive,nuclear/transcription positive (n=11); Group 1, ribosomal/mitochondrialpositive, IFN negative, nuclear/transcription negative (n=21); Group 2,ribosomal/mitochondrial negative, IFN positive, nuclear/transcriptionnegative (n=25); and Group 3, ribosomal/mitochondrial positive, IFNpositive, nuclear/transcription negative (n=24). The significance of anassociation between a clinical feature and a particular subgroup wasestimated by comparing the patients belonging to that subgroup againstall other patients using a chi-squared test for binary clinicalvariables and an unpaired t-test for continuous variables.

A number of clinical features were associated with patient group 3(positive for both ribosomal/mitochondrial and IFN signatures butnegative for nuclear/transcription signature, FIG. 5A). Among the 81patients enrolled in this study, the only incidence of gastrointestinallupus occurred in group 3 (6 of 23 patients, or 26%, p=9.3×10⁻⁵). Thefrequency of alopecia was also significantly higher in group 3 than inthe other groups combined ( 16/23 or 70% of group 3 vs. 24/54 or 44% ofall other patients, p=0.04). Although not significant, there was aslight enrichment of patients with a history of hemolytic anemia ingroup 3 ( 6/23 or 26% of group 3 vs. 5/53 or 9% of all others, p=0.06).While the other patient groups consisted of between 55% and 67%Caucasians, only 25% of the patients in group 3 were Caucasian(p=0.002). This difference was primarily accounted for by an increasedfrequency of African American patients (63% of group 3 vs. 27% of allothers, p=0.007).

In addition to considering single patient subgroups, pairs of subgroupsthat were associated with particular clinical features also wereconsidered as compared to the other two patient subgroups (FIG. 5B).Although the frequency of cerebrovascular accident (CVA) in this SLEpopulation was quite low, the only patients with this complicationoccurred in groups 0 and 3 who exhibited both the IFN andribosomal/mitochondrial signatures with or without thenuclear/transcription signature ( 5/33 or 15% of patients in groups 0and 3 vs. 0/40 other patients, p=0.01). History of osteopenia was morefrequent in groups 2 and 3, whose patients were IFN positive butnuclear/transcription negative with or without theribosomal/mitochondrial signature ( 20/40 or 50% of patients in groups 2and 3 vs. 6/28 or 21% of all other patients, p=0.02). Finally, patientsin groups 1 and 3 (ribosomal positive but nuclear/transcription negativewith or without the IFN signature) were more likely to have had anabnormal liver function test ( 18/43 or 42% of group 1 and 3 vs. 6/34 or18% of all other patients, p=0.02).

Heterogeneity within IFN signature positive patients: The set ofclinical features associated with the IFN signature is described herein.K-means clustering of the lupus patient data revealed three subtypes ofIFN signature positive patients (FIG. 6A): (i) those that also carry theribosomal/mitochondrial signature with the nuclear/transcriptionsignature (group 0), (ii) those that also carry theribosomal/mitochondrial signature in the absence of thenuclear/transcription signature (group 3), and (iii) those that lackboth the ribosomal/mitochondrial and nuclear/transcription signatures(group 2). Experiments were conducted to determine whether some featuresassociated with the IFN signature might be specifically associated withone of these IFN positive subtypes.

A history of proteinuria was not associated with the IFN signature inthis patient population ( 28/57 or 49% of IFN positive patients vs. 6/20or 30% of IFN negative patients, p=0.14). This was surprising, since acorrelation had previously been observed between renal involvement andthe IFN signature. However, the frequency of proteinuria wassignificantly higher in the IFN positive subset that also exhibited boththe ribosomal/mitochondrial and nuclear/transcription signatures (FIG.6B; 8/11 or 73% of group 0 vs. 26/66 or 39% of all other patients,p=0.04).

While the SLEDAI components for low complements and increased anti-DNAantibodies were significantly associated with the IFN signature as awhole, the frequency of these immunologic abnormalities was found to beparticularly high in the IFN positive patients that were negative forthe ribosomal/mitochondrial signatures (FIG. 6C; low complement, 15/25or 60% of group 2 vs. 16/56 or 29% of all other patients, p=0.007;anti-DNA, 18/25 or 72% of group 2 vs. 12/56 or 21% of all otherpatients, p=1.3×10⁻⁵). In the case of anti-DNA antibodies, the p-valuefrom the comparison of group 2 vs. all other patients was even moresignificant than the p-value from the comparison of all IFN positivepatients vs. the IFN negative patients (p=3.7×10⁻⁴). This is consistentwith the observation that the ribosomal/mitochondrial signature isnegatively correlated with the anti-DNA component of the SLEDAI.

Autoantibodies against the RNA-binding proteins Ro and La were alsocorrelated with the IFN signature as a whole, although for anti-La thecorrelations did not reach statistical significance (Table 10). Theseautoantibodies were particularly associated with the IFN positivepatients that were also positive for the ribosomal/mitochondrialsignature but lacked the nuclear/transcription signature (FIG. 6D;anti-Ro, 13/23 or 57% of group 3 vs. 12/52 or 23% of all other patients,p=0.003; anti-La, 8/23 or 35% of group 3 vs. 4/52 or 8% of all otherpatients, p=0.005). Consideration of only the group 3 subset of IFNpositive patients provided the statistical significance for anti-La thatwas lacking when the IFN signature was considered as a whole.

The requirement for cytotoxic therapy has been shown to be associatedwith the IFN signature (Table 10). However, the frequency of patients inthe IFN positive group having received cytotoxic therapy was notsignificantly higher than the frequency of IFN negative patientsrequiring cytotoxic drugs ( 36/56 or 64% of IFN positive patients vs.7/17 or 41% of IFN negative patients, p=0.09). Subsetting of the IFNpositive patients in FIG. 6E revealed that the need for cytotoxictherapies was primarily associated with groups 0 and 2 ( 25/33 or 76% ofgroups 0 and 2 vs. 18/40 or 45% of all other patients, p=0.008).

To assess the degree of similarity between various gene expressionsignatures, signature scores were used to calculate correlationcoefficients between each pair of signatures (Table 14). Summarystatistics for the signatures used in FIGS. 5-6 are provided in Table15.

TABLE 14 Correlations between gene signatures Full Condensed Ribosomal/Nuclear/ Ribosomal mitochondrial mitochondrial mitochondrial NeutrophilIFN transcription Ribosomal 1.00 0.87 0.89 0.99 0.35 −0.17 −0.30 Full —1.00 0.98 0.93 0.43 0.11 −0.51 mitochondrial Condensed — — 1.00 0.950.44 0.07 −0.51 mitochondrial Ribosomal/ — — — 1.00 0.38 −0.10 −0.37mitochondrial Neutrophil — — — — 1.00 0.32 −0.36 IFN — — — — — 1.00−0.24 Nuclear/ — — — — — — 1.00 transcription Data are presented asr-values from the comparison of the indicated pairs of expressionsignature scores.

TABLE 15 Summary statistics for gene signatures Ribosomal/ Nuclear/mitochondrial Neutrophil IFN transcription SLE 8.4 ± 4.4 4.6 ± 2.0 26.9± 12.0  8.8 ± 3.1 Control 4.2 ± 1.3 2.2 ± 0.5 12.8 ± 2.7 14.5 ± 2.6p-value 1.4 × 10⁻¹² 3.2 × 10⁻¹⁷  1.1 × 10⁻¹⁶  7.7 × 10⁻¹⁸ Datasummarizing the indicated signature scores are presented as mean ±standard deviation, with p-value obtained from an unpaired t-test (SLEpatients vs. controls).

Example 3 Identifying Genes that can be Used to Monitor and Predict SLEActivity

Collection of specimens for a human lupus biorepository was initiated.This study was designed to identify biomarkers for SLE. A lupusbiorepository contains samples collected from the Hopkins Lupus CohortStudy (Petri et al., Arthritis Rheum 34:937-44 (1991)), in which over1,000 SLE patients are being followed, with clinic visits scheduledevery three months. This study was designed to follow 300 patients forone year, including collection of clinical data and blood and urinesamples at each visit during the enrollment year. The repositorycurrently contains samples from over 1,350 individual patient visits of297 enrolled SLE patients.

Study participants, clinical data, and biological specimens: Informedconsent was obtained from each participant. A comprehensive medicalhistory taken during the first visit of the study included a baselineSLICC/ACR damage index, which scores irreversible organ damageattributed to SLE (Gladman et al., Arthritis Rheum 39:363-9 (1996)).Detailed clinical data collected and recorded during each visit includedseveral measures of disease activity: the SLE Disease Activity Index(SLEDAI; Bombardier et al., Arthritis Rheum 35:630-40 (1992)) which isweighted by organ system; the Systemic Lupus Activity Measure (SLAM;Liang et al., Arthritis Rheum 32:1107-18 (1989)) which grades symptomsand laboratory manifestations by severity; the British Isles LupusAssessment Group measure (BILAG; Hay et al., Q J Med 86:447-58 (1993))which reflects the physician's intention to treat based onorgan-specific involvement; and a physician's global assessment (PGA)which is recorded on a 3 cm visual analog scale and represents theexpert's judgment of clinical disease activity. Clinical data alsoincluded a medication history and a battery of clinical laboratorytests. Biological samples collected at each visit included RNA(extracted from whole blood using the PAXgene system fromQiagen/Becton-Dickinson), DNA, serum, plasma, peripheral bloodmononuclear cells (cryopreserved), and urine. Clinical data collectedprior to the beginning of the study were available in most cases, anddata collection continued after the last study visit. For many of thesepatients, prospective clinical data extending over two and a half yearswere available.

Clinical features of SLE patients: The clinical spectrum of disease atbaseline for the first 81 patients enrolled in the SLE study issummarized in FIG. 7, left panels. The patients demonstrated a range ofclinical disease activity as measured by the SLEDAI (FIG. 7A, leftpanel) and by PGA (FIG. 7B, left panel). Renal involvement was observedin 37% of the patients, arthritis occurred in 28% of the patients,hematologic involvement occurred in 22% of the patients, and 17% of thepatients had a rash (FIG. 7C, left panel). Most patients (77%) weretaking the anti-malarial drug hydroxychloroquine, 64% were treated withsteroids, and 41% were taking various immunosuppressive drugs (FIG. 7D,left panel). The patients were followed prospectively for 1.5 to 2.5years, and cumulative statistics for the subsequent study visits werecomparable with the baseline visit data (FIG. 7, right panels).

Processing of samples and microarrays: Blood from each of 81 SLEpatients and 41 healthy controls was drawn into four PaxGene tubes(PreAnalytix, Franklin Lakes, N.J.). Total RNA was isolated according tothe manufacturer's protocol, and on-column DNase treatment wasperformed. RNA yield and integrity were assessed using an AgilentLab-on-a-Chip Bioanalyzer (Agilent Technologies, Inc., Palo Alto,Calif.). cRNA probes were generated and hybridized to Affymetrix U133AGeneChips according to standard Affymetrix protocols (ExpressionAnalysis Technical Manual, Affymetrix, Santa Clara, Calif.). Seven ofthe 81 cRNA samples, generated using RNA from SLE patients, required tworounds of amplification. Following hybridization, the microarrays werewashed, stained, and scanned. Affymetrix Microarray Suite 5.0 softwarewas used to generate expression (or “signal”) values for each gene afternormalizing the microarrays by scaling the overall intensity of eachmicroarray to 1500.

Gene markers for SLE activity: Microarray data were analyzed to identifygenes associated with SLE disease activity. The correlation coefficientbetween each gene on the chip and the baseline visit SLEDAI wascalculated. Using relatively stringent criteria (r>0.3, p<0.01), aninitial group of 156 genes associated with disease activity wasidentified. Raw data for these 156 genes are presented in Table 16.Hierarchical clustering of the data was performed using Cluster andTreeView software (Eisen et al., Proc Natl Acad Sci USA 95:14863-8(1998)). Prior to clustering, each expression value was divided by themean signal of the 41 control subjects, and the log₂ of this ratio wasused as input data for the Cluster software. Hierarchical clustering ofthe data revealed two prominent clusters, an IFN signature and adistinct immunoglobulin (Ig) signature. The genes comprising these twoclusters were among those that correlated most strongly with currentSLEDAI.

TABLE 16 156 genes whose expression correlated with current SLEDAI (r >0.3, p < 0.01) Accession No. Gene NM_006529 glycine receptor, alpha 3NM_002477 myosin, light polypeptide 5, regulatory NM_006399 basicleucine zipper transcription factor, ATF-like NM_006701 thioredoxin-like4A NM_003315 DnaJ (Hsp40) homolog, subfamily C, member 7 BC003186 DNAreplication complex GINS protein PSF2 NM_000125 estrogen receptor 1U37025 sulfotransferase family, cytosolic, 1A, phenol-preferring, member1 U28169 sulfotransferase family, cytosolic, 1A, phenol-preferring,member 2 AI984980 chemokine (C-C motif) ligand 8 S69738 chemokine (C-Cmotif) ligand 2 NM_013276 carbohydrate kinase-like BE407516 cyclin B1AF109196 chloride intracellular channel 4 NM_004349 core-binding factor,runt domain, alpha subunit 2; translocated to, 1; cyclin D-relatedBC000795 signal-transducing adaptor protein-2 AA931929 AA931929NM_005609 phosphorylase, glycogen; muscle (McArdle syndrome, glycogenstorage disease type V) AK025862 AK025862 NM_017723 hypothetical proteinFLJ20245 AF010446 major histocompatibility complex, class I-relatedNM_003104 sorbitol dehydrogenase NM_006394 regulated in glioma BC005220chaperonin containing TCP1, subunit 8 (theta) BF674842 thymine-DNAglycosylase NM_018444 protein phosphatase 2C, magnesium-dependent,catalytic subunit D26121 splicing factor 1 NM_002757 mitogen-activatedprotein kinase kinase 5 AL049748 RNA binding motif protein 9 AF241788nuclear distribution gene C homolog (A. nidulans) NM_000900 matrix Glaprotein AF216650 methylthioadenosine phosphorylase NM_001374deoxyribonuclease I-like 2 NM_021057 interferon, alpha 7 AF074264 lowdensity lipoprotein receptor-related protein 6 AF339807 Transcribedlocus, moderately similar to NP_955751.1 potassium channel regulator[Homo sapiens] AL117546 Transcribed locus, weakly similar to NP_079012.2gasdermin domain containing 1 [Homo sapiens] NM_002933 ribonuclease,RNase A family, 1 (pancreatic) NM_014498 golgi phosphoprotein 4NM_001271 chromodomain helicase DNA binding protein 2 NM_006683 familywith sequence similarity 12, member A NM_000290 phosphoglycerate mutase2 (muscle) AI380850 AI380850 AA211481 LIM domain binding 3 AI553791microtubule-associated protein 4 NM_001481 growth arrest-specific 8AI017382 ataxin 7-like 1 /// ataxin 7-like 1 AK021474 AK021474 AW083357interleukin 1 receptor antagonist AF283773 WD repeat domain 23 NM_002753mitogen-activated protein kinase 10 AW024233 glycine-N-acyltransferaseNM_024046 hypothetical protein MGC8407 NM_002418 motilin AI133721AI133721 X05610 collagen, type IV, alpha 2 NM_017545 hydroxyacid oxidase(glycolate oxidase) 1 NM_004854 carbohydrate sulfotransferase 10AL022068 AL022068 AB051447 AB051447 NM_012434 solute carrier family 17(anion/sugar transporter), member 5 AV728958 talin 2 NM_005925 meprin A,beta NM_000761 cytochrome P450, family 1, subfamily A, polypeptide 2NM_002759 protein kinase, interferon-inducible double stranded RNAdependent BC005354 BC005354 BC000606 BC000606 NM_018579 mitochondrialsolute carrier protein AJ249377 AJ249377 AI252582 AI252582 BC000603BC000603 AW303136 AW303136 AI557312 AI557312 AK022897reversion-inducing-cysteine-rich protein with kazal motifs NM_000770NM_000770 NM_000243 Mediterranean fever N35896 PTPRF interactingprotein, binding protein 1 (liprin beta 1) X60502 sialophorin (gpL115,leukosialin, CD43) U39945 adenylate kinase 2 BC004467 enthoprotinNM_013324 cytokine inducible SH2-containing protein BC0013622′,3′-cyclic nucleotide 3′ phosphodiesterase AF040105 chromosome 6 openreading frame 108 M62898 annexin A2 pseudogene 2 BC005902 biliverdinreductase A /// biliverdin reductase A NM_003896 sialyltransferase 9(CMP-NeuAc:lactosylceramide alpha-2,3- sialyltransferase; GM3 synthase)NM_001643 apolipoprotein A-II AK026273 AK026273 M27968 fibroblast growthfactor 2 (basic) M12350 M12350 NM_014221 mature T-cell proliferation 1BF002474 BF002474 AA521272 AA521272 NM_000429 methionineadenosyltransferase I, alpha AF043294 BUB1 budding uninhibited bybenzimidazoles 1 homolog (yeast) X84340 X84340 AW405975 Ig lambda lightchain variable region AF043586 Immunoglobulin lambda constant 2(Kern-Oz-marker) X93006 Immunoglobulin lambda light chain V region(Humla203) /// Anti- HIV-1 gp120 immunoglobulin E51 lambda light chain/// Immunoglobulin lambda constant 2 (Kern-Oz-marker) /// Immunoglobulinlambda variable group /// Hepatitis B surface antigen antibody variabledomain D87021 Ig lambda-chain V-J-C region (HCV-65) AF043583 IgG toPuumala virus G2, light chain variable region BG482805 Anti-HIV-1 gp120V3 loop antibody DO142-10 light chain variable region L14457 L14457AJ249377 Immunoglobulin lambda joining 3 M20812 Similar to Ig kappachain X79782 Hypothetical protein similar to KIAA0187 gene productM87790 Anti-HIV-1 gp120 immunoglobulin E51 lambda light chain D84140D84140 AW408194 immunoglobulin kappa variable 1D-13 AJ408433 AJ408433BG540628 BG540628 U80139 IgM rheumatoid factor RF-SB1, variable heavychain L34164 immunoglobulin heavy constant gamma 1 (G1m marker) ///immunoglobulin heavy constant gamma 1 (G1m marker) AA476303 AA476303AF078844 AF078844 BF246115 metallothionein 1F (functional) NM_030641apolipoprotein L, 6 NM_001295 chemokine (C-C motif) receptor 1 AW008051agrin NM_006084 interferon-stimulated transcription factor 3, gamma 48kDa NM_017523 XIAP associated factor-1 BC002666 guanylate bindingprotein 1, interferon-inducible, 67 kDa /// guanylate binding protein 1,interferon-inducible, 67 kDa NM_003113 nuclear antigen Sp100 BF217861metallothionein 1E (functional) NM_002450 metallothionein 1X N53555Sialoadhesin AA749101 interferon induced transmembrane protein 1 (9-27)AL121994 AL121994 BF338947 interferon induced transmembrane protein 3(1-8U) AJ243797 three prime repair exonuclease 1 NM_005138 SCOcytochrome oxidase deficient homolog 2 (yeast) AL031602 AL031602AF333388 AF333388 NM_005951 metallothionein 1H NM_017414 ubiquitinspecific protease 18 NM_001549 interferon-induced protein withtetratricopeptide repeats 3 NM_002534 2′,5′-oligoadenylate synthetase 1,40/46 kDa NM_016817 2′-5′-oligoadenylate synthetase 2, 69/71 kDaNM_002462 myxovirus (influenza virus) resistance 1, interferon-inducibleprotein p78 (mouse) /// myxovirus (influenza virus) resistance 1,interferon- inducible protein p78 (mouse) NM_006820 chromosome 1 openreading frame 29 NM_005101 interferon, alpha-inducible protein (cloneIFI-15K) NM_004030 interferon regulatory factor 7 NM_005953 NM_005953NM_005950 metallothionein 1G NM_002463 myxovirus (influenza virus)resistance 2 (mouse) AI862559 hypothetical protein FLJ11286 NM_000062serine (or cysteine) proteinase inhibitor, Glade G (C1 inhibitor),member 1, (angioedema, hereditary) NM_001953 endothelial cell growthfactor 1 (platelet-derived) BC006333 tripartite motif-containing 14 ///tripartite motif-containing 14 NM_001188 BCL2-antagonist/killer 1NM_018541 NM_018541 NM_017853 thioredoxin-like 4B AA457021BCL2-associated athanogene 5 AA669336 coagulation factor C homolog,cochlin (Limulus polyphemus) N92920 N92920 X65232 zinc finger protein 79(pT7)

The IFN signature, which showed a tight clustering of 35 transcripts,was found in 60 of the 81 cases (74%). Nearly all of the genes in thissignature were also identified in a comparison of the 81 patients with agroup of 41 controls.

The Ig signature identified in the initial gene list consisted of 18immunoglobulin loci transcripts. This signature was suspected to reflectthe presence of plasma cells in blood (Ginsburg et al., Clin Exp Immunol35:76-88 (1979); Harada et al., Br J Haematol 92:184-91 (1996); Dornerand Lipsky, Lupus 13:283-9 (2004)). Therefore, a larger set oftranscripts associated with current SLEDAI (r>0.19, p<0.05, n=1219genes) was used to identify additional members of the Ig signature. Thisanalysis identified 37 transcripts, 32 of which encoded the constant andvariable regions of the kappa and lambda light chains, as well as IgM,IgD, and IgG heavy chains (represented by multiple probesets).Transcripts for the plasma cell specific surface marker CD38 were foundin the expanded cluster. Another gene in the cluster, thethioredoxin-related gene TXNDC5, is a downstream target of X-box bindingprotein 1 (XBP-1; Shaffer et al., Immunity 21:81-93 (2004)). XBP-1 is atranscriptional regulator required for plasma cell differentiation(Reimold et al., Nature 412:300-7 (2001)). XBP-1 mRNA levels werecorrelated with SLEDAI (r=0.26, p=0.02) and with the other Ig signaturetranscripts (r=0.55, p=1.0×10⁻5), however XBP-1 did not cluster tightlywith the other Ig/plasma cell transcripts. The expression of BLIMP-1,which regulates expression of XBP-1 in B cells (Shaffer et al., Immunity17:51-62 (2002)), was not significantly correlated with either currentSLEDAI (r=0.06, p>0.1) or with the level of Ig transcripts (r=−0.01,p>0.1). Three additional genes in the expanded Ig/plasma cell cluster(LOC91316, LOC91353 and KIAA0746) are not yet well characterized. Rawdata for the genes comprising the Ig signature are presented in Table17.

TABLE 17 37 Ig signature transcripts Accession No. Gene AA522514KIAA0746 protein Z00008 Ig kappa variable 1D-8 BG340548 IgM VDJ-regionNM_001775 CD38 antigen (p45) AJ275469 Ig heavy constant delta BG540628HRV Fab N8-VL D87021 Ig lambda-chain V-J-C region (HCV-65) D84140 Iglambda variable 3-21 AA398569 similar to Ig lambda-like polypeptide 1L14457 Ig rearranged kappa-chain gene V-J-region AW408194 Ig kappavariable 1D-13 BG482805 Anti-HIV-1 gp120 V3 loop antibody DO142-10AF103530 Ig kappa light chain variable region M87789 Ig heavy constantgamma 1 (G1m marker) L14458 Ig rearranged kappa-chain gene V-J-regionAL022324 LOC91353 BG485135 Anti-rabies virus Ig rearranged kappa chainV-region BC005332 Ig kappa constant M87790 Anti-HIV-1 gp120 Ig E51lambda light chain X57812 Ig lambda constant 2 (Kern-Oz-marker)NM_030810 thioredoxin domain containing 5 M85256 Cationic anti-DNAautoantibody AF103529 Ig kappa light chain variable region D84143 Ig(mAb59) light chain V region AJ249377 Ig lambda joining 3 X51887 Igkappa variable 1/OR2-108 X79782 Hypothetical protein similar to KIAA0187gene product M20812 similar to Ig kappa chain AJ408433 Ig kappa chainvariable region BG536224 HRV Fab N8-VL AF043583 IgG to Puumala virus G2,light chain variable region X93006 IgG lambda light chain V-J-C regionL23516 IgG heavy chain V region U80139 IgM rheumatoid factor RF-SB1,variable heavy chain M24669 Ig heavy constant mu AF047245 Ig lambdalight chain VJ region AJ239383 IgM rheumatoid factor RF-TT9, variableheavy chain

Strong Ig/plasma cell signatures were found in 33 of the 81 baselinevisits (41%). In all cases, the Ig/plasma cell signature was associatedwith the IFN signature. An Ig/plasma cell signature ‘score’ was derivedfor each patient. The Ig/plasma cell score was based on the 37immunoglobulin transcripts (CD38, TXNDC5, 32 Ig transcripts, and 3 othergenes), the expression levels of which were highly correlated withcurrent disease activity as measured by SLEDAI. The Ig/plasma cellsignature score was calculated by first normalizing the expressionvalues for each row (Table 17) so that the maximum value in any row was1.0. The columns (Table 17) were then summed to obtain the score.Several additional methods for calculating gene expression signaturescores were also explored, and all yielded highly similar results (datanot shown; see Baechler et al., Proc Natl Acad Sci USA 100:2610-5(2003)). Individuals with high levels of the immunoglobulin transcripts(N=33) had an Ig/plasma cell score of 11.5±5.8 (mean±SD), compared to4.7±1.3 in the remaining patients (N=48) (p=1.3×10⁻⁷). For comparison, agroup of 41 matched controls showed an average Ig score of 5.3±1.7(p=7.2×10⁻⁷ versus Ig-positive SLE patients; p=not significant versusIg-negative SLE; p=6.4×10⁻⁴ versus all SLE).

In addition to its correlation with current SLEDAI, the Ig/plasma cellscore was also significantly correlated with disease activity asmeasured by PGA and other measurements associated with active lupus,including elevated erythrocyte sedimentation rate (ESR) and low WBC andhematocrit (Table 18). Active renal disease was also associated with theIg/plasma cell signature. There were modest correlations between theIg/plasma cell score and use of certain medications (current use of ACEinhibitors, and historical use of immunosuppressive drugs andhydroxychloroquine). Anti-dsDNA antibodies were strongly correlated withthe signature, suggesting that some of the plasma cells identified maybe producing these antibodies. There was a significant correlationbetween African American ethnicity and the Ig/plasma cell signature,perhaps reflecting the increased prevalence of severe lupus in AfricanAmerican patients (Alarcon et al., Arthritis Rheum 41:1173-80 (1998)).

TABLE 18 Clinical features correlated with the Ig signature  r-valuewith Clinical feature Ig score p-value LR^(A) p-value RP^(B) SLEDAI 0.36p = 9.6 × 10⁻⁴ p = 0.004 PGA 0.33 p = 0.002 p = 0.001 ESR 0.33 p = 0.004p = 0.003 WBC −0.23 p = 0.04 p = 0.04 Renal 0.25 p = 0.02 p = 0.02Hematocrit −0.35 p = 0.001 p = 3.7 × 10⁻⁴ ACE-inhibitor −0.22 p = 0.05 p= 0.03 Hx^(C) cytotoxic drugs 0.21 p = 0.07 p = 0.05 Hx plaquenil −0.22p = 0.04 p = 0.003 Hx low C3 0.22 p = 0.05 p = 0.01 Anti-dsDNA Abs 0.34p = 0.002 p = 0.01 Hx of anti-dsDNA Abs 0.20 p = 0.08 p = 0.02 Hxleukopenia 0.28 p = 0.01 p = 0.003 Hx anemia 0.23 p = 0.04 p = 0.01Ethnicity (African 0.38 p = 4.1 × 10⁻⁴ p < 1 × 10⁻⁵ American)^(A)p-value determined by linear regression analysis ^(B)p-valuedetermined by random permutation ^(C)Hx, history

In a parallel discovery path, the patient group was divided based on theSystemic Lupus Activity Measure-Revised (SLAM-R) disease activity index(Liang et al., Arthritis Rheum 32:1107-18 (1989); Bae et al., Lupus10:405-9 (2001)), or a combination of PGA and the SLEDAI. Geneexpression patterns were compared between 25 patients with high SLAM-Rscores (≧5) and 25 patients with low scores (≦2). The following criteriawere used to identify differentially expressed genes: (i) p<0.05 byunpaired student's t-test, (ii) average fold change of at least 1.5 whencomparing the mean of active patients to the mean of inactive patients,and (iii) absolute difference of at least 100 signal units whencomparing the means of the two groups. Of the 521 genes that weredifferentially expressed between these two patient groups (data notshown), the gene list included 15 Ig/plasma cell transcripts and 56IFN-inducible genes. Similarly, a comparison of gene expression betweenpatients with active disease by another definition (PGA ≧1.5 and SLEDAI≧3, n=22) and patients with inactive disease (PGA ≦1 and SLEDAI ≦2,n=21) identified 344 transcripts, which included both IFN-responsive(n=35) and Ig/plasma cell genes (n=18). Together, these data providefurther evidence for an association between the IFN and Ig/plasma cellsignatures and active SLE.

Gene expression signatures and the prediction of future diseaseactivity: Genetic algorithm testing (Gibson, Biosystems 23:219-28;discussion 229 (1989)) was applied to the set of SLEDAI-associatedgenes. The list of 1219 SLEDAI-associated genes was used as input forthe genetic algorithm software (Agillence Software, Inc., Savage,Minn.). Ten genes were identified that showed the strongest correlationswith SLEDAI. Raw data for the genes comprising the GA-10 signature arepresented in Table 19. This set of genes included a representative fromboth the IFN signature (interferon induced transmembrane protein 1) andthe Ig/plasma cell signature (HRV Fab N8-VL, kappa light chain variableregion). An expression score (GA-10 score) based on these 10 genes wascalculated as described above for the Ig/plasma cell signature. Asexpected, the GA-10 score was strongly correlated with current visitSLEDAI (r=0.85, p<1×10⁻⁵), exceeding the highest correlation between anysingle transcript and current SLEDAI (Ig λ joining 3, r=0.48, p=0.0001).

TABLE 19 GA-10 signature genes Accession No. Gene BC005902 biliverdinreductase A NM_022162 caspase recruitment domain family, member 15AA669336 coagulation factor C homolog, cochlin NM_013255 muskelin 1,intracellular mediator containing kelch motifs U34919 ATP-bindingcassette, sub-family G (WHITE), member 1 BG540628 HRV Fab N8-VL BF002474CTD small phosphatase-li AL512697 Similar to C10orf94 protein NM_001384DPH2-like 2 (S. cerevisiae) AA749101 interferon induced transmembraneprotein 1 (9-27)

Patients were then sorted based on initial visit GA-10 scores, anddisease activity, as measured by SLEDAI and PGA in the initial andsubsequent visits to the clinic, was examined. Visualization of futuredisease activity, as measured by SLEDAI or PGA, was performed usingTreeView software (Eisen et al., Proc Natl Acad Sci USA 95:14863-8(1998)). There was a strong positive correlation between the initialvisit GA-10 score and the maximum future SLEDAI observed in follow-upvisits (r=0.48, p=3.0×10⁻⁵). The GA-10 score also showed predictivevalue for future disease activity as measured by PGA at future visits(r=0.27, p=0.009). To assess the significance of the correlation betweeninitial visit signature scores and the maximum future activity score(SLEDAI or PGA), p-values were calculated both by linear regression andby random permutation analysis of the dataset. The concordance ofp-values generated using the two methods was very high, and the p-valuesreported reflect those obtained by random permutation.

The SLEDAI measurement alone at the baseline visit showed predictivevalue for future maximum SLEDAI (r=0.39, p=6.9×10⁻⁴), suggesting thatactive disease at any given visit is predictive of future diseaseactivity. A subset of the patient group was examined that consisted onlyof those cases where the initial visit SLEDAI was low (SLEDAI ≦3, n=38patients). In these patients with quiescent baseline disease activity, apositive and significant correlation was also observed between baselinevisit GA-10 score and maximum future disease activity as measured bySLEDAI (r=0.32, p=0.03) or PGA (r=0.28, p=0.05).

Genetic algorithm gene groups of less than 10 transcripts showed reducedpredictive power in these analyses (Table 20). Furthermore, the GA-10score exhibited a higher correlation with maximum future activity(SLEDAI and PGA) than either the IFN or Ig signature alone (unpublisheddata).

Measuring gene expression levels for key blood cell transcripts at asingle baseline clinic visit can be informative for current visit lupusdisease activity and can be used in predicting the future course ofdisease.

TABLE 20 Ten-gene score yields higher correlation with current andfuture activity compared with scores based on fewer genes CurrentCurrent Max future Max future SLEDAI PGA SLEDAI PGA # of r- r- p- r- r-p- genes value p-value value value value p-value value value 1 0.48 6.0× 10⁻⁵  0.27 0.006 0.25 0.02 0.25 0.01 2 0.63 <1 × 10⁻⁵ 0.21 0.03 0.310.006 0.05 0.31 3 0.70 <1 × 10⁻⁵ 0.26 0.01 0.33 0.004 0.11 0.17 4 0.75<1 × 10⁻⁵ 0.17 0.06 0.46 1.0 × 10⁻⁴ 0.24 0.02 5 0.79 <1 × 10⁻⁵ 0.270.007 0.33 0.003 0.15 0.10 10 0.85 <1 × 10⁻⁵ 0.30 0.003 0.48 3.0 × 10⁻⁵0.27 0.009

Example 4 Neutrophil Gene Expression Signature in Human SLE

The neutrophil signature correlates with the percentage and absolutenumber of neutrophils as determined by concurrent complete blood count(CBC). The mitochondrial signature is also correlated with theneutrophil signature (r=0.42, p=0.0001), indicating that it derives, inpart, from neutrophils, which are producers of oxidants. The neutrophilsignature appears to be associated with current visit and historicalevidence for renal disease. To identify genes associated with renallupus, gene expression profiles were compared between patients with ahistory of renal disease (n=43) and patients with no renal involvement(n=38). One hundred and thirty three genes were identified that met thefollowing criteria for differential expression: (i) p<0.05, (ii) averagefold change >1.5, and (iii) absolute difference >100 signal units. Thesegenes are listed in Table 21.

One gene cluster was identified that included several neutrophil genes(e.g., alpha-defensins, azurocidin). Investigation of the expression ofthese genes in purified cells from control donors demonstrated thatthese genes, which were observed to be highly expressed in patients withrenal lupus, are neutrophil-specific in their expression. These resultsdemonstrate that the genes listed in Table 21 can be used to identifymammals having renal lupus.

TABLE 21 Additional neutrophil signature Accession No. Gene BC003629RNA, U2 small nuclear AI221950 leucine-rich repeat protein, neuronal 3NM_002145 homeo box B2 NM_001870 carboxypeptidase A3 (mast cell)AF063002 four and a half LIM domains 1 AC003682 zinc finger protein 134(clone pHZ-15) NM_025081 KIAA1305 protein AJ003062 spindle pole bodyprotein AU147182 Ras responsive element binding protein 1 NM_002238potassium voltage-gated channel, subfamily H, member 1 NM_000174glycoprotein IX (platelet) BC001090 MICAL-like 1 NM_001279 celldeath-inducing DFFA-like effector a AF061194 ectodermal dysplasia 1,anhidrotic AK026820 ST3 beta-galactoside alpha-2,3-sialyltransferase 1BC005956 relaxin 1 (H1) AF116771 tumor protein 63 kDa with stronghomology to p53 NM_020484 NM_020484 BG426689 Thyroid hormone receptorassociated protein 2 AF070541 hypothetical protein LOC284244 NM_022146neuropeptide FF 1; RFamide-related peptide receptor AK022765alpha-methylacyl-CoA racemase NM_024819 hypothetical protein FLJ22955AL136545 transient receptor potential cation channel, subfamily M,member 3 NM_003159 serine/threonine kinase 9 NM_022842 CUB domaincontaining protein 1 NM_012098 angiopoietin-like 2 M88162oculocerebrorenal syndrome of Lowe NM_004933 cadherin 15, M-cadherin(myotubule) AW165979 Zinc finger protein 609 X81637 H. sapiens clathrinlight chain b gene NM_005142 gastric intrinsic factor (vitamin Bsynthesis) AK027173 Ring finger protein 24 NM_001878 cellular retinoicacid binding protein 2 NM_014344 four jointed box 1 (Drosophila) U54826MAD, mothers against decapentaplegic homolog 1 (Drosophila) NM_025012hypothetical protein FLJ13769 NM_002472 myosin, heavy polypeptide 8,skeletal muscle, perinatal AF052145 chromosome 2 open reading frame 10L77561 DiGeorge syndrome gene D AI538172 Retinoblastoma binding protein6 BE875592 vesicle docking protein p115 BG421209 DEAD/H(Asp-Glu-Ala-Asp/His) box polypeptide 24 AU147620 AU147620 AI685892fasciculation and elongation protein zeta 2 (zygin II) AV684285hypothetical protein FLJ20719 L06147 golgi autoantigen, golgin subfamilya, 2 AA664291 SON DNA binding protein BF965566 leucine rich repeat (inFLII) interacting protein 1 AI679073 IQ motif containing GTPaseactivating protein 1 AA699583 ARP2 actin-related protein 2 homolog(yeast) AI809341 protein tyrosine phosphatase, receptor type, C AI472757NS1-associated protein 1 AW117498 forkhead box O1A (rhabdomyosarcoma)NM_006260 DnaJ (Hsp40) homolog, subfamily C, member 3 U14383 mucin 8,tracheobronchial AL121890 chromosome 20 open reading frame 30 AF339787Glypican 5 AK022663 similar to Hypothetical zinc finger protein KIAA1956AF207990 fer-1-like 3, myoferlin (C. elegans) NM_000804 folate receptor3 (gamma) R25849 825849 NM_000756 corticotropin releasing hormoneNM_030929 Kazal-type serine peptidase inhibitor domain 1 AL031230glycosylphosphatidylinositol specific phospholipase D1 AB040897 RANbinding protein 10 NM_017593 homolog of mouse BMP-2 inducible kinaseNM_003851 cellular repressor of E1A-stimulated genes NM_000240 monoamineoxidase A T51252 transmembrane and coiled-coil domain family 2 R60866transcription factor Dp-1 AL132665 BCL2/adenovirus E1B 19 kD interactingprotein 3-like NM_006121 keratin 1 (epidermolytic hyperkeratosis)NM_002094 G1 to S phase transition 1 AL046979 Tensin 1 X77737 solutecarrier family 4, anion exchanger, member 1 AF117233 makorin, ringfinger protein, 1 AA133341 Chromosome 14 open reading frame 87 NM_019094nudix (nucleoside diphosphate linked moiety X)-type motif 4 NM_021083Kell blood group precursor (McLeod phenotype) NM_000140 ferrochelatase(protoporphyria) NM_001738 carbonic anhydrase I NM_030758 oxysterolbinding protein 2 AL031178 F-box protein 9 AL035301 phosphatidylinositolglycan, class C AL049381 Pre-B-cell leukemia transcription factor 1AA583044 bone morphogenetic protein 2 NM_003696 olfactory receptor,family 6, subfamily A, member 1 NM_005193 caudal type homeo boxtranscription factor 4 X90763 keratin, hair, acidic, 5 NM_002317 lysyloxidase AI884858 Putative prostate cancer tumor suppressor NM_019060NICE-1 protein AF005081 chromosome 1 open reading frame 68 X06409v-raf-1 murine leukemia viral oncogene homolog 1 NM_014154 HSPC056protein AF338650 PDZ domain containing 3 AB000277 discs, large(Drosophila) homolog-associated protein 1 AK024328 ATP-binding cassette,sub-family A (ABC1), member 1 AI435747 chromosome 21 open reading frame2 AI762174 zinc finger protein 42 (myeloid-specific retinoicacid-responsive) NM_002886 RAP2B, member of RAS oncogene family AV705938neuronal Shc adaptor homolog D84109 RNA-binding protein gene withmultiple splicing AL121873 ubiquitin-conjugating enzyme E2 variant 1NM_006980 transcription termination factor, mitochondrial AF306765aspartate beta-hydroxylase NM_020415 found in inflammatory zone 3 L33930CD24 antigen (small cell lung carcinoma cluster 4 antigen) NM_001925defensin, alpha 4, corticostatin NM_004084 defensin, alpha 1,myeloid-related sequence NM_001700 azurocidin 1 (cationic antimicrobialprotein 37) M18728 carcinoembryonic antigen-related cell adhesionmolecule 6 L35848 membrane-spanning 4-domains, subfamily A, member 3NM_000607 orosomucoid 1 NM_018324 thioesterase domain containing 1NM_001721 BMX non-receptor tyrosine kinase NM_003855 interleukin 18receptor 1 NM_022746 MOCO sulphurase C-terminal domain containing 1NM_003596 tyrosylprotein sulfotransferase 1 NM_000045 arginase, liverNM_004633 interleukin 1 receptor, type II BF513244 Dishevelledassociated activator of morphogenesis 2 BC000903 high-mobility group(nonhistone chromosomal) protein 2 AA910946 adaptor-related proteincomplex 1, mu 2 subunit NM_020995 haptoglobin-related protein AF233437myotubularin related protein 3 AC005390 glutathione peroxidase 4(phospholipid hydroperoxidase) AL524520 G protein-coupled receptor 49BE748563 Hypothetical protein BC015148 NM_007017 SRY (sex determiningregion Y)-box 30 BC005896 hyaluronoglucosaminidase 3 NM_001262cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4)

Other Embodiments

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

1. A method for identifying a mammal having severe systemic lupuserythematosus, said method comprising (a) determining whether or not amammal contains cells having an IFN signature 1, and (b) classifyingsaid mammal as having severe systemic lupus erythematosus if said mammalcontains said cells and classifying said mammal as not having severesystemic lupus erythematosus if said mammal does not contain said cells.2. The method of claim 1, wherein said mammal is a human.
 3. The methodof claim 1, wherein said cells are peripheral blood mononuclear cells.4. A method for assessing systemic lupus erythematosus disease activity,said method comprising (a) determining whether or not a mammal containscells having an activity signature 1, an activity signature 2, or anactivity signature 3, and (b) classifying said mammal as having activesystemic lupus erythematosus disease if said mammal contains said cellsand classifying said mammal as not having active systemic lupuserythematosus disease if said mammal does not contain said cells.
 5. Themethod of claim 4, wherein said mammal is a human.
 6. The method ofclaim 4, wherein said cells are peripheral blood mononuclear cells. 7.The method of claim 4, wherein said method comprises determining whetheror not said mammal contains cells having said activity signature
 1. 8.The method of claim 4, wherein said method comprises determining whetheror not said mammal contains cells having said activity signature
 2. 9.The method of claim 4, wherein said method comprises determining whetheror not said mammal contains cells having said activity signature
 3. 10.A method for identifying a mammal likely to experience active systemiclupus erythematosus disease, said method comprising (a) determiningwhether or not a mammal having systemic lupus erythematosus diseasecontains cells having an activity signature 3, and (b) classifying saidmammal as being likely to experience said active systemic lupuserythematosus disease if said mammal contains said cells and classifyingsaid mammal as not being likely to experience said active systemic lupuserythematosus disease if said mammal does not contain said cells. 11.The method of claim 10, wherein said mammal is a human.
 12. The methodof claim 10, wherein said cells are peripheral blood mononuclear cells.13. A method for identifying a mammal likely to respond to an anti-IFNtreatment for systemic lupus erythematosus, said method comprising (a)determining whether or not a mammal having systemic lupus erythematosusdisease contains cells having an IFN signature 1, and (b) classifyingsaid mammal as being likely to respond to said anti-IFN treatment ifsaid mammal contains said cells and classifying said mammal as not beinglikely to respond to said anti-IFN treatment if said mammal does notcontain said cells.
 14. The method of claim 13, wherein said mammal is ahuman.
 15. The method of claim 13, wherein said cells are peripheralblood mononuclear cells.